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The largest Phragmites reed field in the world, with a historical area of approximately 1000 km 2 , is located in the Liaohe Delta in northeastern China. The Phragmites wetlands are extensively managed to maximize the production of reed biomass for the paper industry. Based on satellite remote sensing we estimated that the total area of the Phragmites wetlands has decreased from 857 km 2 in 2003 to 786 km 2 in 2009 to accommodate oil field infrastructure and societal developments. However, at the same time the production of Phragmites biomass used for the production of reed pulp has increased to 400,000 metric tons per year. This paper describes the great efforts that have been made to increase the Phragmites yields for the paper industry, including (1) diversion of freshwater from rivers to the Phragmites fields, (2) management of the water table, (3) harvesting and burning for pest control, and (4) seawater irrigation to rehabilitate Phragmites fields infested with weeds. The paper industry has facilitated the conservation of the Phragmites wetlands and their associated ecosystem services. Besides being a source for fiber, the wetlands provide important habitat for wildlife, sequester carbon, and create job opportunities and economic income for the local people. [ABSTRACT FROM AUTHOR]

• Improved lorenz chaotic systems with higher complexity. • Pixel permutation method based on the image pyramid structure. • Secure and efficient image encryption method. • Galois field multiplication for efficient diffusion of pixels. This paper proposes an improved Lorenz chaotic system and a secure and efficient image encryption method to enhance encryption effectiveness in encrypted images. The proposed improved Lorenz chaotic system addresses the problem of applying the Lorenz chaotic system to image encryption, resulting in weak chaotic characteristics and susceptibility to reconstruction. Dynamic analysis, sensitivity analysis, and randomness testing demonstrate that the improved Lorenz chaotic system exhibits hyperchaotic characteristics, with a maximum Lyapunov exponent of 2.9897. Based on the improved Lorenz chaotic system, this paper proposes an image encryption method that combines image pyramid structure permutation and Galois field diffusion. Unlike most of the current permutation methods limited to a single image layer, this paper proposes a multilayer permutation method based on the image pyramid structure to enhance the permutation effect of image encryption. Although diffusion based on Galois field multiplication operation is efficient and secure, it is less effective in encrypting pixel points with a pixel value of '0′. To address this issue, this paper incorporates DNA computing into diffusion based on Galois field operations, enabling even pure black images to achieve better encryption effectiveness. Experimental results demonstrate that the encryption method proposed in this paper effectively conceals information contained in the plain image. The global Shannon entropy of the encrypted Lena image can reach 7.9975, indicating a high level of randomness and complexity. Notably, even a slight alteration, such as changing a single pixel, results in a significant divergence, with 99.6307 % of the cipher image's pixels being distinct. Moreover, it effectively withstands analysis from various attacks. Therefore, the encryption method proposed in this paper can be effectively applied to grayscale image encryption scenarios requiring relatively high security and encryption efficiency, such as remote sensing image encryption and personal privacy image encryption. [ABSTRACT FROM AUTHOR]

The new generation of remote sensing satellites improves the coverage of remote sensing imaging by improving the orbital surface and using the rotary scanning imaging technology. In this paper, a new technical scheme of rotating payload platform based on disturbance-free payload technology is proposed to adapt to the rotary scanning imaging satellite. The platform adopts the cubic Stewart configuration and uses the Lorentz force magnetic actuator as the actuator to form the satellite support module and the payload into a noncontact two-stage three-body. First, this paper uses Newton-Euler method to establish a six-degree-of-freedom dynamic model for the new rotating payload platform, and establishes a kinematic model by quaternion method. Then, according to the established kinematics and dynamics model, the control scheme of absolute pointing control of payload platform (upper platform) and relative follow-up control of kinetic energy container (middle platform) is designed. Furthermore, the six-degree-of-freedom decoupling control method of the actuator is designed. Based on this, the absolute pointing controller of the payload module and the relative position and attitude calculate controller of the kinetic energy container are designed. Finally, the research of simulation is carried out. The simulation results show that the new rotating payload platform has higher rotation pointing accuracy and stability than the traditional ultra-quiet platform, and the vibration isolation acting on the support module and the vibration absorption acting on the payload are more effective. [ABSTRACT FROM AUTHOR]

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels' contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold. [ABSTRACT FROM AUTHOR]

Cropland non-agriculturalization (CNA) refers to the conversion of cropland into construction land, woodland/garden/grassland, water body, or other non-agricultural land, which ultimately disrupts local agroecosystems and the cultivation and production of crops. Remote sensing technology is an important tool for large-area CNA detection, and remote sensing based methods that can be used for this task include the time-series analysis method and change detection from bi-temporal images. In particular, change detection methods using high-resolution remote sensing imagery have great potential for CNA detection, but enormous challenges do still remain. The large intra-class variance of cropland with different phenological stages and planting patterns leads to cropland areas being difficult to identify effectively, while certain features can be misidentified because they are similar to cropland, resulting in false alarms and missed detections in the results. There is also a lack of large-scale CNA datasets covering multiple change scenarios as data support. To address these problems, a lightweight model focused on CNA detection (CNANet) is proposed in this paper. Specifically, the uniquely crafted represent-consist-enhance (RCE) module is seamlessly integrated between the encoder and decoder components of CNANet to perform a contrast operation on the deep features extracted by the feature extractor. The RCE module is specifically designed to aggregate multiple cropland representations and extend the cropland representations from the confusing background, to achieve the purpose of reducing the intra-class reflectance differences and enhancing the model's perception of cropland. In addition, a large-scale high-resolution cropland non-agriculturalization (Hi-CNA) dataset was built for the CNA identification task, with a total of 6797 pairs of 512 × 512 images with semantic annotations. Compared to the existing datasets, the Hi-CNA dataset has the advantages of multiple phenological stages, multiple change scenarios, and multiple annotation types, in addition to the large data volume. The experimental results obtained in this study show that the benchmark methods tested on the Hi-CNA dataset can all achieve a good accuracy, proving the high-quality annotation of the dataset. The overall accuracy and F1-score of CNANet with the default settings reach 93.81 % and 78.9 %, respectively, achieving a superior accuracy, compared to the other benchmark methods, and demonstrating stronger perception of cropland changes. In addition, in two selected verification regions within the large-scale real-world CNA mapping results, the F1-score is 83.61 % and 50.87 %. The Hi-CNA can be downloaded from http://rsidea.whu.edu.cn/Hi-CNA_dataset.htm. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-sensing image acquisition and analysis in recent years. It has brought promising results in low-altitude monitoring tasks that require detailed visual inspections. Semantic segmentation is one of the hot topics in UAV remote sensing image analysis, as its capability to mine contextual semantic information from UAV images is crucial for achieving a fine-grained understanding of scenes. However, in the remote sensing field, recent reviews have not focused on combining "UAV remote sensing" and "semantic segmentation" to summarize the advanced works and future trends. In this study, we focus primarily on describing various recent semantic segmentation methods applied in UAV remote sensing images and summarizing their advantages and limitations. According to the distinction in modeling contextual semantic information, we have categorized and outlined the methods based on graph-based contextual models and deep-learning-based models. Publicly available UAV-based image datasets are also gathered to encourage systematic research on advanced semantic segmentation methods. We provide quantitative results of representative methods on two high-resolution UAV-based image datasets for fair comparisons and discussions in terms of semantic segmentation accuracy and model inference efficiency. Besides, this paper concludes some remaining challenges and future directions in semantic segmentation for UAV remote sensing images and points out that methods based on deep learning will become the future research trend. • Summarize semantic segmentation methods and datasets for UAV remote sensing images. • Provide fair comparisons of representative methods in accuracy and efficiency. • Identify challenges and future directions for UAV image semantic segmentation. • Semantic segmentation methods based on deep learning will be the mainstream. [ABSTRACT FROM AUTHOR]

• This paper discusses and addresses the main challenges of evaluating the socio-economic impact of high-tech public infrastructures using Earth observations (EO). • The EO infrastructure employs an array of cutting-edge technologies that ultimately contribute to understanding, analysing and managing different natural and societal aspects of Earth with a tremendous socio-economic impact on current and future generations. However, this impact is challenging to understand and quantify. • This paper reviews the current fragmented literature on the socio-economic impact of high-tech infrastructure and builds on a new stream of studies on the cost–benefit analysis of large-scale research science-based infrastructures and on investing in the space economy. • This new evaluation framework estimates EO infrastructures' benefits from the perspective of different types of social agents, directly or indirectly involved, along the value chain. • The framework can be adapted to estimate the socio-economic returns of other high-tech infrastructure investments. This paper addresses the main challenges of evaluating the socio-economic impact of high-tech infrastructures, using Earth observation (EO) as an example. EO is a critical domain of the space economy, providing valuable insights into planet Earth's natural and societal aspects. As national agencies invest in high-tech infrastructures like EO, there is a growing need for evaluate their socio-economic returns (not to be confused with their financial returns). However, there is no clear consensus on how to assess such social impact. Building on a new field of studies of social cost–benefit analysis of research infrastructures and the socio-economic impact of investment in the space economy, we propose a new evaluation framework that considers the various stakeholders along the EO value chain. This approach can be adapted to evaluate the socio-economic returns of other high-tech public infrastructures, such as telescopes, particle accelerators, genomic platforms, synchrotron light sources, supercomputers and cloud infrastructures. [ABSTRACT FROM AUTHOR]

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, i.e. Jiangsu, Yosemite, and London. Experimental results show that AdaTreeFormer significantly surpasses the state of the art, e.g. in the cross domain from the Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of the absolute counting errors and an increase of 10.8% in the accuracy of the detected trees' locations. The codes and datasets are available at https://github.com/HAAClassic/AdaTreeFormer. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with imperceptible but aggressive perturbations. This paper studies the synthetic-to-measured (S2M) transfer setting, where an attacker generates adversarial perturbation based solely on synthetic data and transfers it against victim models trained with measured data. Compared with the current measured-to-measured (M2M) transfer setting, our approach does not need direct access to the victim model or the measured SAR data. We also propose the transferability estimation attack (TEA) to uncover the adversarial risks in this more challenging and practical scenario. The TEA makes full use of the limited similarity between the synthetic and measured data pairs for blind estimation and optimization of S2M transferability, leading to feasible surrogate model enhancement without mastering the victim model and data. Comprehensive evaluations based on the publicly available synthetic and measured paired labeled experiment (SAMPLE) dataset demonstrate that the TEA outperforms state-of-the-art methods and can significantly enhance various attack algorithms in computer vision and remote sensing applications. Codes and data are available at https://github.com/scenarri/S2M-TEA. [ABSTRACT FROM AUTHOR]

Abstract: This paper described a convenient semiquantitative method for colorimetric detection of protein with self-calibration integrated on the test strip. Hydrophilic paper was employed as microfluidic device for running colorimetric assay, tree-shaped design was developed to ensure uniform microfluidic flow for multiple branches. The approach was validated with bovine serum albumin (BSA) colorimetric detection, and colorimetric results observed by naked eyes were consistent with that from apparatus. The device could be coupled with digital transmission of images for remote monitoring system for diagnosis, food control, and environmental analysis. [Copyright &y& Elsevier]

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity. [ABSTRACT FROM AUTHOR]

[Display omitted] The increasing availability of remote sensing imagery led to the emergence of numerous burned area mapping products and methodologies, thus, the necessity of spatial and temporally validating them. The main objective of this paper is to conduct a global analysis and comparison of MODIS and VIIRS active fire dates with dating provided by moderate resolution burned area mapping products. As reference burned area maps, we used the Burned Area Reference Database (BARD) to analyze and compare the performances of MODIS and VIIRS active fires. For 316 path-rows, we calculated the temporal gap between the dates of BARD pixels and those of the five nearest active fires. Olson terrestrial biomes were taken into account in the analysis of results. The agreement rates between the dates of burned areas and of active fires varied based on their geographical distance, fire size and biome. The highest agreement rates were observed for smaller distances (<500 m) and larger fires (>100 ha). Boreal and Mediterranean biomes exhibited the highest values of date percent agreement between burned area pixels and active fires, while the lowest values were found in biomes encompassed by grasslands and savannas. [ABSTRACT FROM AUTHOR]

Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature lines as geometric primitives to achieve polygonal building extraction by recovering topological relationships among these lines within an individual building. To extract building feature lines with precision, we adopt a two-stage strategy that combines Convolutional Neural Network (CNN) and transformer architectures. A CNN-based module extracts preliminary feature lines, which serve as positional priors for initializing positional queries in the subsequent transformer-based module. For polygonal building contour reconstruction, we devise a learnable polygon topology reconstruction module that predicts adjacency relationships among discrete lines, and integrates lines into building polygons. The resultant building polygons, based on feature lines, exhibit inherent regularity that aligns with manual labeling standards. Extensive experiments on the Vectorizing World Buildings dataset, the WHU aerial building dataset and the WHU-Mix (vector) dataset validate Line2Poly's impressive performance in building feature line extraction and instance-level building detection. Moreover, Line2Poly's predictions exhibit the highest level of concurrence with manual delineations, with over 83% agreement on the WHU aerial building test set and 68.7/59.7% on the WHU-Mix (vector) test set I and II, respectively. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSI-SC) is crucial for various high-level applications, including RSI retrieval, image captioning, and object detection. Deep learning-based methods can accurately predict scene categories. However, these approaches often require numerous labeled samples for training, limiting their practicality in real-world RS applications with scarce label resources. In contrast, few-shot remote sensing image scene classification (FS-RSI-SC) has garnered substantial research interest owing to its potential to mitigate the need for extensive training samples. In recent years, there has been a surge in studies on FS-RSI-SC. This paper presents a comprehensive overview of FS-RSI-SC research, categorizing existing methods into two groups. The first group comprises approaches based on data augmentation, transfer learning, metric learning, and meta-learning. Our analysis reveals that most existing FS-RSI-SC methods fall into the meta-learning category, employing attention mechanisms, self-supervised learning (SSL), and feature fusion techniques for enhanced performance. Additionally, transfer learning-based methods consistently outperform other approaches in this category. The second group is centered around large-scale pre-training, which has demonstrated remarkable competitiveness across various tasks, including FS-RSI-SC. This special group of methods has shown considerable potential and is expected to attract more attention with the increasing popularity of large-scale pre-training and the unimodal and multimodal foundation models. Moreover, we proposed a pipeline that harnesses the capabilities of powerful large vision-language models (VLMs) as image encoders, establishing new baselines for FS-RSI-SC on commonly used datasets under standard experimental settings. Our empirical results validated the effectiveness of utilizing large VLMs and highlighted their potential for FS-RSI-SC. Through a joint analysis of state-of-the-art methods and our experiments with VLMs, we identified the prevailing challenges in FS-RSI-SC and outlined promising directions for future research. [ABSTRACT FROM AUTHOR]

Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation are knowledge-driven symbolism and data-driven connectionism. Knowledge-driven approaches are certain and theoretically sound, which consider the regularities, formulae with accompanying theorems, and expert knowledge. However, it is difficult to address huge-scale RS data and complex nonlinear problems. The data-driven paradigm has stronger data learning and representation ability, yet poor interpretability, low trustworthiness of interpretation results, and reliance on massive labeled data. To address these limitations, we argue that current RS intelligent interpretation requires the guidance of geographic ideas and a unified theoretical framework rather than the independent use of remotely sensed data, duplicative development of interpretation models, or isolated applications of geographic information extraction. Thus, we introduce the concepts of trustworthy RS interpretation (TRSI) with multi-granularity of space, time, and attribute to facilitate the understanding of the morphology, types, and indicators (visual perception) and the perspective of the structure, evolution, and trends (geographical cognition) of complex geographical space. Second, integrated technologies of the TRSI with multiple scales and stages are developed, including a pixel-level visual perception, a geo-parcel-level quantitative inversion, and a scene-level geographical cognition. Finally, a RS big data interpretation system with quantitative semantic parsing is designed to support precise applications with cloud–edge-end collaboration. Rather than a simple overview and accumulation of traditional RS intelligent interpretation methods, this paper analyzes current challenges in RS interpretation and proposes new concepts, technologies, and applications of TRSI. It will empower us to perceive the complex geographical space comprehensibly and reveal its structural composition and evolutionary mechanism, serving geospatial decision applications such as evaluation, planning, and forecasting. [ABSTRACT FROM AUTHOR]

Land-cover information reflects basic Earth's surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively simple and lack flexibility, limiting the generalizability for complex remote sensing scenes. In this paper, we propose a Land-cOVEr mapping Neural Architecture Search framework (LoveNAS) to automatically find efficient decoders that are compatible with the encoders and tasks. Specifically, LoveNAS introduces a hierarchical dense search space, including densely connected layer-level and multi-scale operation-level search spaces. The search spaces contain independent connection and operation fusion strategies, facilitating sufficient interaction of multi-scale features. After searching based on large-scale datasets, a series of pre-trained encoders and adaptive decoders are obtained. These can be smoothly applied to multi-scene tasks using weight-transfer network training. Experimental results on normal and disaster scenes shows that LoveNAS outperforms 16 handcrafted architectures and NAS methods. Some searched structures coincide with the existing advanced artificial designs, revealing the potential value of LoveNAS in network design and guidance. Group's website: http://rsidea.whu.edu.cn/resource%5fsharing.htm. GitHub page: https://github.com/Junjue-Wang/LoveNAS. [ABSTRACT FROM AUTHOR]

The state-of-the-art methods for cloud detection are dominated by deep convolutional neural networks (DCNNs). However, it is very expensive to train DCNNs for cloud detection and the trained DCNNs do not always perform well as expected. This paper proposes a shallow CNN (SCNN) by removing pooling/unpooling layers and normalization layers in DCNNs, retaining only three convolutional layers, and equipping them with 3 filters of 1 × 1 , 1 × 1 , 3 × 3 in spatial dimensions. It demonstrates that the three convolutional layers are sufficient for cloud detection. Since the label output by the SCNN for a pixel depends on a 3 × 3 patch around this pixel, the SCNN can be trained using some thousands 3 × 3 patches together with ground truth of their center pixels. It is very cheap to train a SCNN using some thousands 3 × 3 patches and to provide ground truth of their center pixels. Despite of its low cost, SCNN training is stabler than DCNN training, and the trained SCNN outperforms the representative state-of-the-art DCNNs for cloud detection. The same resolution of original image, feature maps and final label map assures that details are not lost as by pooling/unpooling in DCNNs. The border artifacts suffering from deep convolutional and pooling/unpooling layers are minimized by 3 convolutional layers with 1 × 1 , 1 × 1 , 3 × 3 filters. Incoherent patches suffering from patch-by-patch segmentation and batch normalization are eliminated by SCNN without normalization layers. Extensive experiments based on the L7 Irish, L8 Biome and GF1 WHU datasets are carried out to evaluate the proposed method and compare with state-of-the-art methods. The proposed SCNN promises to deal with images from any other sensors. The code and data will be open for further research. 1 1 https://github.com/dfchai/SCNN. [ABSTRACT FROM AUTHOR]

• A multimodal computational approach was used for the 3D survey of a panel painting. • Data from close-range photogrammetry and laser microprofilometry were fused. • The warping arrow before and after restoration was quantified on the 3D point cloud. • Multi-temporal monitoring of the painting is computed via Cloud-to-Cloud distance. • Micrometric roughness of paint layers is measured by laser microprofilometry. This paper reports the results obtained from the workflow designed for the 3D survey of a Renaissance panel painting using close-range photogrammetry and conoscopic laser microprofilometry, before and after its restoration. The artwork under examination is the central panel of a Renaissance polyptych realized by a late Gothic painter active in central Italy in the late 15th century. This painting has had a rather troubled conservation history following numerous adverse events that have severely damaged it structurally. In preparation for restoration work, the fusion of point cloud data derived from active and passive sensors served to create a single multi-resolution model on which to conduct advanced metrological analyses for multi-temporal monitoring of the changes undergone by the panel painting during and after structural restoration. [ABSTRACT FROM AUTHOR]

• Extract ancient city wall sites at pixel level from LiDAR remote sensing data based on deep learning. • Improvement strategies in the dataset generation, model training, and post-processing steps. • A significant source of inspiration for the inspection, conservation, and monitoring of cultural heritage. Ancient city walls, one of the most notable distinguishing features of Chinese ancient cities, are military defenses constructed of rammed earth. The ancient city walls have considerable study value because they served as the city's boundary and a symbol of power. However, as a result of natural erosion and human activities, many sites have been ruined. Existing optical remote sensing technologies, LiDAR point cloud processing algorithms, and deep learning methods are inadequate for the extraction and segmentation of ancient city wall sites. The novel semantic segmentation method for ancient city wall sites is described in this paper that extracts sites at the pixel level from LiDAR remote sensing data based on deep learning. To begin, the point cloud data collected by airborne laser scanning is processed into DEM data, and the distribution of ancient city walls in the study area is obtained through archaeological survey and expert interpretation. The dataset for deep learning semantic segmentation is then generated using image cropping and data augmentation techniques. Third, implement a U-Net semantic segmentation framework for microtopographic archaeological sites, and predict ancient city wall sites in the testing region after model training. Finally, the deep learning results are optimized using the connected component analysis method, and prediction mistakes such as holes and noise are removed. Taking Jinancheng, the capital city of the Chu kingdom, as an example, the proposed method process can identify and extract the ancient city wall sites at the pixel level, where the evaluation metrics reach 94.12% (Precision) and 81.38% (IoU). The experiment results are excellent due to improvement strategies in the dataset generation, model training, and post-processing steps. Thus, this study is significant for the current survey and protection of ancient city wall sites. The source code will be freely available at https://github.com/wshunli/Open-CHAI-CityWalls. [ABSTRACT FROM AUTHOR]

Neural Radiance Fields (NeRF) is a technique for reconstructing real-world scenes from multiple views. However, existing methods mostly focus on the visual effects of scene reconstruction while neglecting geometric accuracy, which is crucial in photogrammetry and remote sensing. In this paper, we propose a method called LiDeNeRF which uses LiDAR point cloud to provide depth priors for NeRF reconstruction. The goal of LiDeNeRF is to achieve real-time rendering of NeRF scenes and 3D reconstruction results with high geometric accuracy. In this method, first, the LiDAR point cloud is projected onto images to generate a sparse depth map. Then, by triangulating the sparse depth and using a multi-view image depth propagation method, a dense depth map with high accuracy is quickly generated as the depth prior for NeRF. Finally, a new depth correction module is designed and embedded into the NeRF rendering pipeline to improve the accuracy of scene depth estimation. The experimental results evince that our methodology has attained paramount performance in both novel view synthesis and 3D reconstruction tasks. Our source code is made available at https://github.com/WPC-WHU/LiDeNeRF. [ABSTRACT FROM AUTHOR]

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the diversity in the ensemble classification results primarily relies on different DL models. However, training the DL models consumes a significant amount of time, and training multiple networks not only incurs additional time costs but also affects the overall efficiency. To address this, a new approach has been proposed in this paper, which takes advantage of the relationships between pixels and their combinations to generate diverse classification results. It's a novel ensemble classification framework, termed as the Doublet-Based Ensemble Classification Framework (DBECF), which eliminates the need for multiple classifiers. The DBECF starts by utilizing the training set to combine different samples to generate doublets. Then, features are assigned to these doublets through an exponentiation operation, resulting in a doublet training set. Using both the original training set and the derived doublet datasets, the DBECF is trained. For each input pixel, the DBECF produces multiple classification results, which are then integrated to obtain a more accurate output. To validate the proposed approach, experiments were conducted on three datasets, including multispectral images, hyperspectral images, and time series images. The maximum accuracies achieved by DBECF on the three datasets are 87.80 %, 97.71 %, and 83.51 %, respectively. In comparison to the contrastive methods, the incremental improvements in accuracy are 3.73 %, 7.66 %, and 9.16 %, respectively. The experimental results indicate that no matter using DL or non-deep learning for training, our proposed framework achieves progress on accuracy improvement outperforming classifications using comparative approach that based on single instance. This research provides a new perspective on the combination of DL and ensemble learning, highlighting its important implications and practical value in enhancing classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Newly constructed areas (NCA) are continuously emerging with the development of urbanization, which, however, triggered a series of ecological and environmental issues. Therefore, monitoring NCA is of great significance for sustainable urbanization. Extraction of NCA from remote sensing images involves semantic segmentation of constructed areas and change detection. However, the acquisition of samples for training change detection models is time-consuming. In this study, we proposed a multi-task learning framework including unsupervised change detection and supervised semantic segmentation for NCA extraction. The main contributions of this study are: (1) A deep multivariate alternation detection (DMAD) algorithm was proposed for unsupervised change detection of hyperspectral images. By introducing the optimization objective of Multivariate Alternation Detection (MAD) in the loss functions, the features of changed areas can be extracted more effectively; (2) A multi-task learning framework was proposed for the joint training of DMAD change detection and supervised semantic segmentation, where the two modules were mutually optimized via semantic masks and consistency loss. Based on Orbita Hyperspectral Satellites (OHS) images, we conducted experiments in two cities (Qinzhou and Wuzhou) of Guangxi province. The experimental results indicated that the proposed method can achieve the F1-score better than existing unsupervised change detection methods (e.g., DCCA and DSFA) by an average of 15%, and the OA better than existing semantic segmentation methods (e.g., U-Net and FreeNet) by an average of 1%. The F1-score and Kappa for NCA extraction reached above 0.80 for both study areas. The implementation of this paper will be made available at https://github.com/tulilin/Multitask_NCA. [ABSTRACT FROM AUTHOR]

Multimodal change detection (CD) is an increasingly interesting yet highly challenging subject in remote sensing. To facilitate the comparison of multimodal images, some image regression methods transform one image to the domain of the other image, allowing for images comparison in the same domain as in unimodal CD. In this paper, we begin by analyzing the limitations of previous image structure based regression models that only rely on similarity relationships. Then, we highlight the significance of incorporating dissimilarity relationships as a complementary approach to more comprehensively characterize and utilize the image structure. In light of this, we propose a novel method for multimodal CD called Similarity and Dissimilarity induced Image Regression (SDIR). Specifically, SDIR construct a similarity based k-nearest neighbors (KNN) graph and a dissimilarity based k-farthest neighbors (KFN) graph, where the former mainly characterizes the low-frequency information and the latter captures the high-frequency information in spectral domain. Therefore, the proposed SDIR that incorporates similarity (low-frequency) and dissimilarity (high-frequency) relationships enables to achieve better regression performance. After completing the image regression, we utilize a Markovian based fusion segmentation model to combine the change fusion and change extraction processes for improving the final CD accuracy. The proposed method's effectiveness is demonstrated through experiments on six real datasets and compared with eleven advanced and widely used methods, achieving 5.6% improvements in the average Kappa coefficient. The source code is accessible at https://github.com/yulisun/SDIR. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle-based hyperspectral imaging (HSI) and machine learning (ML) techniques are applied for crop field data collection such as yield, nitrogen content, leaf chlorophyll, biomass estimation, leaf area index (LAI), and biotic and abiotic stress. It is very difficult to identify with certainty best practices for specific applications and for targeted crop species because the results are frequently contradictory; models or indices sometimes perform well, but other times they do not. ML and HSI are useful and sophisticated techniques, but treating them as first-option methods may be detrimental in the long term. Favoring HSI in situations where a multispectral sensor performs equally well only delays end-user adoption of technology owing to cost and complexity. The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users. [ABSTRACT FROM AUTHOR]

Open-set semantic segmentation remains yet a challenging task, not only due to the inherent challenges of pixel-wise classification but also the precise segmentation of categories not seen during training. The pursuit of that task is rapidly growing in the Computer Vision community, urging the need to organize the literature. In this paper, we extend our previous work by conducting a more comprehensive systematic mapping of the open-set segmentation literature between January 2001 and January 2023 and proposing a novel taxonomy. Our goal is to provide a broad understanding of current trends for the open-set semantic segmentation (OSS) task defined by existing approaches that may influence future methods. By characterizing methodologies in terms of open-set identification strategies, data inputs, and other relevant aspects, we present a structured view of how researchers are advancing in the field of open-set semantic segmentation. To the best of the authors' knowledge, this is the first systematic review of OSS methods. Moreover, we apply the proposed taxonomy to selected methods for open-set recognition, outlining important similarities and differences of such a closely related field. [Display omitted] • Systematic review of papers related to open-set semantic segmentation for the past 20 years. • The proposed taxonomy aims to organize the literature on open-set segmentation. • Seminal papers on open-set recognition are classified under the proposed taxonomy. • Applications like autonomous driving and remote sensing were found to commonly resort to the open set strategy. • Methods tackling open-world are becoming more commom; [ABSTRACT FROM AUTHOR]

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel I mbalance K nowledge- D riven Multi-modal Net work (IKD-Net) to extract features from multi-modal heterogeneous data of aerial images and LiDAR directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the G lobal K nowledge- G uided (GKG) and C lass K nowledge- G uided (CKG) gated modules. The whole network then is optimized using a joint loss function. While we were developing IKD-Net, we also established a new dataset called the N ational Agriculture Imagery Program and 3 D Elevation Program C ombined dataset in California (N3C-California) , which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission. Our code and N3C-California dataset are available at https://github.com/wymqqq/IKDNet-pytorch. [ABSTRACT FROM AUTHOR]

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S 3 FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer's input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net. • A subspace similar recombination module is proposed. • The selectable self-attention spectral feature extraction module is designed. • The multi-stream Gist spatial feature extraction module is introduced. [ABSTRACT FROM AUTHOR]

This paper presents the design, modeling, analysis, and test of a 2-degree-of-freedom (2-DOF) high-precision parallel pointing mechanism capable of carrying a heavy load up to 30 N ⋅ m driven by high voltage piezo-stack actuators (HVPSAs). The proposed pointing mechanism, which employs flexure hinges as joints, provides two tilting degrees of freedom and can be utilized for intersatellite optical communication, remote sensing satellite lens pointing, and other engineering fields requiring large load capability. In this paper different kinds of flexure hinges are utilized to achieve the desired motion and working performance. With consideration of most compliant elements, Castigliano's second theorem was adopted to establish the analytical static model of the mechanism, which was then validated by FEA simulation and experimental testing. Finally, a high-precision pointing prototype is fabricated and a series of motion performance tests have been implemented, which validated the feasibility of the proposed system. The superiority of the proposed design is that it possesses large load capacity as well as excellent pointing performance simultaneously, while the former characteristic was infrequently reported in previous works. • Design process of the 2-DOF parallel pointing mechanism is presented. • Castigliano's second theorem was adopted to establish the analytical static model. • Large load capacity up to 30 Nm is achieved by the proposed pointing mechanism. • High precision with resolution of 0.7 μ rad and pointing accuracy of 1.09 μ rad (3 σ). [ABSTRACT FROM AUTHOR]

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area. • This paper provides a review of pan-sharpening methods that adopt four paradigms. • The evaluation of the fused image is also outlined. • We discuss limitations, difficulties, and challenges of pan-sharpening techniques. • The trends in these areas are given for better methodological practices. [ABSTRACT FROM AUTHOR]

Despite the remarkable progress of deep learning-based object extraction in revealing the number and boundary location of geo-objects for high-resolution satellite imagery, it still faces challenges in accurately extracting blurry dense objects. Unlike general objects, blurry dense objects have limited spatial resolution, leading to inaccurate and connected boundaries. Even with the improved spatial resolution and recent boundary refinement methods for general object extraction, connected boundaries may remain undetected in blurry dense object extraction if the gap between object boundaries is less than the spatial resolution. This paper proposes a blurry dense object extraction method named the buffer parsing network (BPNet) for satellite imagery. To solve the connected boundary problem, a buffer parsing module is designed for dense boundary separation. Its essential component is a buffer parsing architecture that comprises a boundary buffer generator and an interior/boundary parsing step. This architecture is instantiated as a dual-task mutual learning head that co-learns the mutual information between the interior and boundary buffer, which estimates the dependence between the dual-task outputs. Specifically, the boundary buffer head generates a buffer region that overlaps with the interior, enabling the architecture to learn the dual-task bias and assign a reliable semantic in the overlapping region through high-confidence voting. To alleviate the inaccurate boundary location problem, BPNet incorporates a high-frequency refinement module for blurry boundary refinement. This module includes a high-frequency enhancement unit to enhance high-frequency signals at the blurry boundaries and a cascade buffer parsing refinement unit that integrates the buffer parsing architecture coarse-to-fine to recover the boundary details progressively. The proposed BPNet framework is validated on two representative blurry dense object datasets for small vehicle and agricultural greenhouse object extraction. The results indicate the superior performance of the BPNet framework, achieving 25.25% and 73.51% in contrast to the state-of-the-art PointRend method, which scored 21.92% and 63.95% in the A P 50 s e g m metric on two datasets, respectively. Furthermore, the ablation analysis of the super-resolution and building extraction methods demonstrates the significance of high-quality boundary details for subsequent practical applications, such as building vectorization. The code is available at: https://github.com/Dingyuan-Chen/BPNet. [ABSTRACT FROM AUTHOR]

• Earthquake anomalies are analyzed for Mw 6.5 Idaho Earthquake America. • Significant anomalies occur in LST, AT, RH, AP and OLR. • Anomalies are studied using statistical, NARX, MLP and CWT methods. • NARX predicted anomalies are more clear than other methods. Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations. [ABSTRACT FROM AUTHOR]

Radio waves propagating the ionosphere are subject to a number of unpredictable and corrupting effects, the most significant of which are phase path reduction, Faraday rotation, and scintillation. A method of measuring these effects is necessary in order to study and understand the solar-terrestrial connections, as well as to anticipate their impact on radio systems. However, limitations in current measurement solutions, most notably the lack of direct ionospheric Faraday rotation measurement capability, restricts the ability to characterize the ionosphere to a sufficient degree for applications such as the detection of coronal mass ejections. The rise in popularity of small satellites presents a new cost-effective opportunity to study and observe the ionosphere in detail, however the challenge of realizing a beacon within the constraints of a small satellite system must be overcome. In this paper, we introduce a measurement technique that builds on past multi-frequency beacons, incorporating the well established differential phase technique, as well as introducing a new differential polarization technique. We achieve this by employing linearly polarized beacon signals, which enables us to separate phase path reduction and Faraday rotation into a phase and polarization error respectively. The introduction of linear polarization overcomes a key limitations in current ionospheric measurement techniques: eliminating errors associated with indirect Faraday rotation measurement. The additional polarization data also enables absolute unambiguous measurement of both parameters with fewer and lower frequencies than conventional solutions, as well as without the need for precise satellite clock, phase biases and positioning data. These characteristics correspond with a reduction in the size, power and complexity of the satellite payload, and as a result, our technique better suited to small satellite applications. To validate the capability of measuring the ionosphere within the constraints of a small satellite package, we establish the influence of system noise, weak scattering, clock errors and antenna characteristics on the measurement technique. We then use this to inform a starting point for a CubeSat beacon and receiver design solution which achieves unambiguous and high-resolution measurement of ionospheric parameters with practicable system requirements and constraints. This starting point demonstrates that a resolution of 12 mTECU and 0.03 rad/m2 for the electron density and Faraday rotation estimates respectively, as well as useful scintillation measurements dominated by scintillation noise may be achieved utilizing a tri-band 3U CubeSat beacon with frequencies 144.2 MHz, 432.6 MHz, and 1297.8 MHz, a transmitter power of no more than 4 watts, and antenna requirements consistent with a dipole type antenna. [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

Broadband optical satellites have been widely used for fine monitoring of coastal waters and inland lakes for their high spatial resolution. Atmospheric correction (AC) is one of the essential data processing steps for remote sensing of water environments with high spatial resolution satellites. Most broadband optical satellites, such as HY-1C Coastal Zone Imager (CZI) with a spatial resolution of 50 m, lack of near-infrared and shortwave infrared bands needed for atmospheric correction, making accurate AC difficult. Auxiliary aerosol data is often needed for accurate AC of broadband optical satellite data, which suffers from the differences in spatial resolution and imaging time between auxiliary data and satellite data. Furthermore, existing AC algorithms always perform AC pixel by pixel, which ignores the spatial relationship between adjacent pixels. Taking HY-1C CZI as an example, this paper proposes a novel atmospheric correction algorithm based on deep learning (SSACNet). Considering the inherent spatial-spectral features of satellite images, the SSACNet combines 2D and 3D convolution. The 3D convolution was used to mine the spatial and spectral features of the image and 2D convolution was explored to perform spatial information compensation. The SSACNet was trained and evaluated using the spatio-temporally synchronized dataset of HY-1C CZI and Landsat8 Operational Land Imager (OLI). The evaluation results by in-situ data show that the SSACNet has good performance, with the average correlation coefficient of 0.89, and the absolute percentage deviation (APD) of four bands ranging from 21.53 % to 35.41 %. Compared with the quasi-synchronous Landsat8 OLI remote sensing reflectance (R rs), the APD is less than 10 %. Compared with traditional atmospheric correction algorithms, SSACNet has significantly improved the spatial and spectral information fidelity. In addition, SSACNet also shows good applicability, as it can be used in both clear ocean waters and turbid coastal waters. This study lays a foundation for the quantitative remote sensing of water environment by broadband optical satellites. [ABSTRACT FROM AUTHOR]

Clouds often appear in remote sensing images, which seriously affect the application of remote sensing images. Therefore, cloud removal is an important preprocessing process in remote sensing image applications. In this paper, we propose a generative adversarial network-based cloud removal method for optical remote sensing images with the assistance of synthetic aperture radar (SAR) images. Our model is an end-to-end model, which consists of a translation module, an attention module, a generator, and a discriminator. We introduce the attention mechanism to accurately locate the cloud regions. With the obtained attention maps as the prior information, the proposed method can remove the clouds while preserving the cloud-free regions. In addition, we include the structural similarity index (SSIM) and the attention penalty in the loss function to improve the performance of the proposed method. Numerical experiments show that the proposed model provides improved cloud removal performance compared with the state-of-the-art methods. • An end-to-end GAN-based network for thick cloud removal in optical images. • SAR image is converted into auxiliary optical image by the translation module. • Attention map obtained to accurately locate regions contaminated by clouds. • The attention mechanism makes the generator focus on recovering the cloudy regions. • Proposal greatly improves cloud removal accuracy compared with competing methods. [ABSTRACT FROM AUTHOR]

Abstract: Although foliar reflectance in the visible wavelengths is largely understood, species-specific relations between leaf spectral properties, pigment content and carbon exchange, and interdependence of these fundamental drivers that ultimately produce large-scale signals complicate understanding of and upscaling in remote sensing applications. We recorded seasonal patterns in foliar reflectance in relation to leaf photosynthetic, biochemical, structural and optical properties in two co-occurring tree species, red oak (Quercus rubra) and paper birch (Betula papyrifera). Over the course of a growing season, we monitored the timing of phenological events, i.e. bud break, near-complete leaf expansion and leaf fall, on mature trees. On a monthly basis, maximum rate of carboxylation (V cmax) and maximum rate of electron transport (J max) were estimated from leaf-level gas exchange measurements in the upper crown for three individuals per species. Thereafter, visible and near infrared spectral properties, nitrogen content and specific leaf area were determined for sampled sunlit leaves. These data were compared with color indices extracted from digital images of sampled leaves throughout the growing season. Studied leaf traits significantly varied between the two species and throughout the growing season. Paper birch was characterized by relatively early bud break and rapid leaf expansion. Hence, interactions between species and day of year could be partly contributed to contrasting spring phenology of paper birch and red oak. Spectral vegetation indices, Chlorophyll Normalized Difference Index (Chl NDI), Photochemical Reflectance Index (PRI) and in particular Red Edge Position (λRE), gave a good indication of leaf physiology over the course of the growing season, more specifically of photosynthesis and leaf nitrogen on an area basis (N area). On the other hand, color indices performed rather poorly at tracking key leaf functional traits in this study. Overall, dark green leaves characterized by low Intensity (I, derived from HSI color space) displayed highest photosynthetic activity and highest values of spectral vegetation indices. [Copyright &y& Elsevier]

The increasing availability of remote sensing data allows dealing with spatial-spectral limitations by means of pan-sharpening methods. However, fusing inter-sensor data poses important challenges, in terms of resolution differences, sensor-dependent deformations and ground-truth data availability, that demand more accurate pan-sharpening solutions. In response, this paper proposes a novel deep learning-based pan-sharpening model which is termed as the double-U network for self-supervised pan-sharpening (W-NetPan). In more details, the proposed architecture adopts an innovative W-shape that integrates two U-Net segments which sequentially work for spatially matching and fusing inter-sensor multi-modal data. In this way, a synergic effect is produced where the first segment resolves inter-sensor deviations while stimulating the second one to achieve a more accurate data fusion. Additionally, a joint loss formulation is proposed for effectively training the proposed model without external data supervision. The experimental comparison, conducted over four coupled Sentinel-2 and Sentinel-3 datasets, reveals the advantages of W-NetPan with respect to several of the most important state-of-the-art pan-sharpening methods available in the literature. The codes related to this paper will be available at https://github.com/rufernan/WNetPan. [ABSTRACT FROM AUTHOR]

Remote sensing imagery allows temporal and large-scale observation of the Earth, and advanced techniques such as deep learning have been developed to deal with the massive data. As a result, by combining remote sensing with deep learning, it is possible to realize change detection, which can reveal the change in land cover and land use. Recent work has focused on semantic change detection (SCD), which simultaneously locates where the changes took place while identifying what objects changed between the different periods. Deep learning based SCD methods are typically based on a multi-task structure, which combines the classification and binary change detection (BCD). In this structure, the change region proposals are derived from the transformation (e.g., concatenation, differencing) of multi-temporal images, but after being processed by a convolutional neural network, there can be an incomplete response in the spatio-temporal features. Because of the high similarity of the color, texture, and geometric information of the ground objects in temporal high-resolution remote sensing imagery, this reduces the distinguishability of features and makes it difficult to identify changed areas. Meanwhile, there is less interaction between the BCD and classification, resulting in semantic inconsistency in changed areas. In this paper, the temporal-agnostic change region proposal network (TCRPN) is proposed, which is based on exploring the saliency of the changed regions in single-temporal images to highlight the changed areas in the spatio-temporal features and improve the response to change. As the changed areas can be the salient regions in the single-temporal images, a single-image salient area extractor (SSAE) is proposed to extract the salient areas from the different temporal images, obtaining salient change region candidates. As the change-related salient regions are temporally agnostic, temporal saliency fusion is used to integrate the candidates extracted from the single-temporal images to obtain a more reliable salient area map, where the salient area map is used to enhance the spatio-temporal features. Meanwhile, a shortcut is proposed to further make the change representation more distinguishable and suppress incorrectly highlighted regions. Directly utilizing multi-temporal images for change region proposal can reduce the change information loss and increase the interaction between the classification and BCD tasks to obtain better spatio-temporal features. The TCRPN is simple and flexible, and can be plugged into any multi-task SCD network. We selected four advanced multi-task SCD methods as the baseline, and added the TCRPN in their encoder or decoder part to conduct comprehensive experiments on three large-scale SCD datasets. The experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD. [ABSTRACT FROM AUTHOR]

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity. [ABSTRACT FROM AUTHOR]

Semantic Segmentation is an important problem in many vision related tasks. Land use and land cover classification involves semantic segmentation of satellite imagery and plays a vital role in many applications. In this paper, we propose an extended U-Net architecture with dense decoder connections and attention mechanism for pixel wise classification of satellite imagery named Attention Dense U-Net (ADU-Net). We further evaluate the effect of different upsampling strategies in the decoder part of the U-Net architecture. We evaluate our models on the Gaofen Image Dataset (GID) for landcover classification consisting of five classes: built-up, forest, farmland, meadow and water. The experiments on the GID dataset show better performance than the previous approaches. Our proposed architecture delivers more than 4% higher mIoU and F1-score than the baseline U-Net. Moreover, our proposed architecture achieves an F1-score of 87.21% and mIoU of 77.66% on the GID dataset. Our evaluations shows that data-dependent upsampling layer achieves higher accuracy than the Transposed Convolution, Pixel Shuffle and Bilinear upsampling layers. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

Mapping lithological units and conducting mineral exploration in a relatively short time and at a reduced cost, requires high-resolution satellite data and state-of-the-art image processing approaches and methods. In this regard, the current paper aims to test an integration of visible-near infrared imagery from WorldView-3 (WV-3) and object-based classification for mapping potential deposits of ocher in Hormuz Island, southern Iran. A combination of field observations, spectroscopy, microscopic mineralogy, and geochemical-based XRD and XRF analyses were conducted on the samples collected throughout the study area. The reflectance and absorption features of the ocher were extracted from spectroscopic measurements of samples from well-known ocher mines at the area, and were then convolved to the WV-3 bands. Results showed that the spectral characteristics of ocher are governed by iron oxides and clay minerals with a presence of distinct strong absorption and high reflectance features in the 510–625 nm and 630–745 nm wavelength regions, respectively. These features are complemented by molecular vibration processes of water O–H intramolecular stretching and H–O–H bending that generate absorption features in the 1440–1940 nm region. Additional absorption features in the 2210–2300 nm are most likely due to the Al-OH and CO3−2 vibrations. The absorptions centered at 480 nm, 540 nm, and 820 nm correspond to bands 2, 3, and 7 of the WV-3, respectively, whereas the high reflectance feature centered near 700 nm corresponds to band 6. These four bands, which were considered as index bands of ocher in this study, were used to assign the segmentation weights and to create the thresholds during image processing. Brightness, density, compactness, and homogeneity features were the primary factors for selecting the training areas in the index bands. The ocher-rich areas were enhanced by using a two-stepapproach of object-based image analysis (OBIA) and image classification, for suitable threshold selection. The information about the feature variables of the ocher within the image and lithological object hierarchy were obtained to evaluate the features of ocher deposits. Information extracted from the index bands provided an important description of the object features, including mean, standard deviation, minimum and maximum pixel values, hue, saturation, and intensity, while the training areas obtained information from the three known ocher mines. Considering the fixed numerical range of ocher mines as a reference, the fixed numerical range of general and specific features of ocher in index bands of WV-3 were achieved. The numerical ranges of pixels were used for creating threshold conditions when applying the "assign class" algorithm in bands 2, 3, and 7 are 815–975 nm, 930–1052 nm, and 1721–1904 nm, respectively. The presented OBIA approach shows a high potential with an overall accuracy of 88 % to discriminate deposits of ocher based on compatibility between ocher mines and identified ocher-bearing pixels. [ABSTRACT FROM AUTHOR]

Following Landsat's practice, Sentinel-2 multispectral satellite products are delivered as raster images projected onto the Universal Transversal Mercator (UTM) spatial reference system, which divides Earth into 60 longitudinal zones. Locally, this guarantees high spatial accuracy, while also easing the interoperability with many regional and governmental datums. On top, the Sentinel-2 product grid uses the Military Grid Reference System (MGRS) tiling scheme to facilitate manageable data slices and straightforward multitemporal image stacking. Although most convenient for small-area applications, activities with a larger geographic scope suffer from this approach and its overhead, as both data duplication and ambiguity appear along UTM zone overlaps and MGRS tile borders. In practice, such areas that are covered by multiple and incongruent grid pixels are known but just tolerated, and their degree has not been measured so far. In this paper, we illuminate the nature and patterns of these overlaps, and calculate the resulting spatial redundancy over the global land surface. We found that the total land area is enlarged in the Sentinel-2 grid definition by 33%, which is a value similar to the simple and single-zoned Plate Carrée projection. The number of co-located grid pixels for a single location ranges from 1 up to 6, with on average more redundancy at mid- and high-latitudes. With regard to global satellite archives in times of big data and increased energy costs, the examined grid appears as a suboptimal choice, inducing complexity and overhead at an unreasonable level. Owing to the grid design, e.g., the yearly Sentinel-2 user product volume (Level-1C and -2A) is inflated by 1 petabyte, entailing cascading downstream costs of storage, bandwidth, and computing. • We scrutinise the geometry of zones and tiles of the Sentinel-2 UTM tiling grid. • UTM-MGRS's grid-inherent overlaps inflate global land remote sensing datasets by 33%. • E.g. yearly Sentinel-2 Level-1C/-2A product volume is increased from 3 to 4 petabytes. • Data increase scales up hardware/energy costs, from dissemination to user application. [Display omitted] [ABSTRACT FROM AUTHOR]

Recent advances in two-view correspondence learning consider aggregating local contextual information from k -nearest neighbors by exploring local geometric extractors using convolution, graph or self-attention. However, there is no guarantee that the extracted local context is conducive to correspondence pruning, since there are typically over 90% outliers in the putative set which would inevitably result in contaminated neighbors and hence contaminated local context. To address this issue, in this paper, we propose the RNA-GNN, an attentional graph neural network (GNN) architecture, which can explicitly model interactions among relevant neighboring correspondences. Specifically, a reciprocal neighbor attention (RNA) module is designed to operate sparse attention on relevant neighbors only rather than all neighbors, mitigating the interference of irrelevant and redundant connectivity, and learning more compact and robust representation. In addition, considering that seeking reliable correspondences requires both local and global context, we introduce a context-aware attention module to extract global and channel-wise context by means of global weighted average pooling with weights that are estimated within the network, excluding outliers from this pooling. Extensive experiments on publicly available datasets demonstrate that RNA-GNN substantially advances current state-of-the-arts on camera pose estimation, homography estimation, and remote sensing image registration. Our code is publicly available at https://github.com/ZizhuoLi/RNA-GNN. [ABSTRACT FROM AUTHOR]

Over the past few years, single image super-resolution (SR) has become a hotspot in the remote sensing area, and numerous methods have made remarkable progress in this fundamental task. However, they usually rely on the assumption that images suffer from a fixed known degradation process, e.g., bicubic downsampling. To save us from performance drop when real-world distribution deviates from the naive assumption, blind image super-resolution for multiple and unknown degradations has been explored. Nevertheless, the lack of a real-world dataset and the challenge of reasonable degradation estimation hinder us from moving forward. In this paper, a self-supervised degradation-guided adaptive network is proposed to mitigate the domain gap between simulation and reality. Firstly, the complicated degradations are characterized by robust representations in embedding space, which promote adaptability to the downstream SR network with degradation priors. Specifically, we incorporated contrastive learning to blind remote sensing image SR, which guides the reconstruction process by encouraging the positive representations (relevant information) while punishing the negatives. Besides, an effective dual-wise feature modulation network is proposed for feature adaptation. With the guide of degradation representations, we conduct modulation on feature and channel dimensions to transform the low-resolution features into the desired domain that is suitable for reconstructing high-resolution images. Extensive experiments on three mainstream datasets have demonstrated our superiority against state-of-the-art methods. Our source code can be found at https://github.com/XY-boy/DRSR • A simple and effective self-supervised degradation representation learning strategy is proposed. • The learned representations can promote cross-domain super-resolution generalization. • A dual-wise modulation network is established for effective feature adaption. • Our method achieves favorable performance on various remote sensing datasets. [ABSTRACT FROM AUTHOR]

Rainbow color scheme is popularly used across the board in many scientific disciplines for visualizing data, yet heavily debated in visualization literature. In this paper, we first report an interdisciplinary visualization survey examining the prevalence rainbow color scheme use in scientific publications including a temporal analysis. Then we consolidate findings from empirical studies on the subject to better understand why and precisely when the rainbow color scheme might impair human performance with visualizations. Consequently, we systematically document and analyze the consequences of using the rainbow color scheme based on over 37,000 figures in 11,808 papers in information visualization, neuroscience, hydrology, geography, remote sensing and planetary science. Our findings reveal that while the rainbow scheme appears less and less in visualization-related publications, it remains popular in other scientific domains including remote sensing and planetary sciences (strikingly fourteen times more frequent in remote sensing and planetary sciences than in information visualization outlets). We also find that conflicting findings about human performance with the rainbow color scheme is most likely explained by what the users are asked to do with it (i.e., task type). We detail and typify tasks used in the related empirical work in an effort to organize the current understanding on the subject, and translate it to practicable recommendations. We believe our review and analyses bring clarity and nuance to "the rainbow debate" and enable better-informed visualization advice. [ABSTRACT FROM AUTHOR]

As one of the central cities in the Wuling Mountains, Huaihua is regarded as the main body and core economic development area in the process of Wuling's future new urbanization, exerting a pivotal influence on the five provincial border areas (Hunan, Hubei, Chongqing, Guizhou and Guangxi). But studies on urban expansion in this city are few, so this paper makes efforts to simulate its urban land expansion by constructing the Cellular Automata model (the Weight-CA model) taking into account differences in land types conversion. Firstly, the paper obtains the time-series Landsat spectral bands (Red, NIR) and NDVI images using the time–space fusion model; then the optimal combination of classification features is screened based on J Bh distance and the object-oriented random forest algorithm is applied for land cover classification; the paper finally constructs the Weight-CA model for the simulation and validation of urban land expansion in Huaihua. Study results in the paper have shown that the Landsat spectral bands obtained with the ESTARFM have a relatively high correlation with real Landsat images. The land cover classification based on the optimal classification feature combination and the object-oriented random forest algorithm is high in accuracy, the total of which exceeding 84 % over 4 years. Meanwhile, based on the Weight-CA model, the simulation accuracy of urban land expansion in Huaihua in 2010 and 2015 reached 89.98 % and 89.60 % compared with the real values. It is operationally ready and practical and of strong practical significance to extract land cover types by Landsat and MODIS data fusion, and simulate urban land expansion using the Weight-CA method. [ABSTRACT FROM AUTHOR]