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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]

Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance. [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]

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]

Remote sensing ship image detection methods have broad application prospects in areas such as maritime traffic and fisheries management. However, previous detection methods relied heavily on a large amount of accurately annotated training data. When the number of remote sensing ship targets is scarce, the detection performance of previous methods is unsatisfactory. To address this issue, this paper proposes a few-shot detection method based on global and local feature aggregation. Specifically, we introduce global and local feature aggregation. We aggregate query-image global and local features with support features. This encourages the model to learn invariant features under varying global feature conditions which enhances the model's performance in training and inference. Building upon this, we propose combined feature aggregation, where query features are aggregated with all support features in the same batch, further reducing the confusion of target features caused by the imbalance between base-class samples and novel-class samples, improving the model's learning effectiveness for novel classes. Additionally, we employ an adversarial autoencoder to reconstruct support features, enhancing the model's generalization performance. Finally, the model underwent extensive experiments on the publicly available remote sensing ship dataset HRSC-2016. The results indicate that compared to the baseline model, our model achieved new state-of-the-art performance under various dataset settings. This model presented in this paper will provide new insights for few-shot detection work based on meta -learning. [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]

• 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]

Abstract: x [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]

This paper investigates the potential of using the Sentinel-2 images for deriving bathymetric maps for the Nile River in Egypt. Regression analysis technique with the aid of in situ measurements were used. Exploring the effects of the size of calibration data, and water depth is another aim of this paper. Linear and machine learning regression techniques (Fine Decision Tree (FDT), and Random Forest (RF) Algorithms) are investigated. The study area is about 23 km between Assiut and Delta barrages. Around 82,000 depth points, are available. Regression models are developed using the depth data and the corresponding digital values of the 13 Sentinel-2 imagery bands. The linear regression model was not applicable (R2 = 0.02). The FDT, and RF models, have (R2 = 0.86) when half of the depth points were used for calibration, and the RMSE of the testing data (the other half of the depth points) equaled to 2.86 and 2.09 m, respectively. When fewer points were used for calibration, the R2 = 0.83 and 0.84, and RMSE = 2.65 and 2.19 m, for FDT and RF, respectively. When only the shallow water areas were considered, the RMSE reached 1.51 and 1.30 m for the FDT and RF, respectively. The VNIR bans of Sentinel-2 images are not enough to estimate the water depth of the Nile river. Collecting in situ measurements for small areas, is adequate for estimating the water depth and produces bathymetry maps for the Nile River in Egypt, and larger in situ measurements are not required. [ABSTRACT FROM AUTHOR]

Satellites engaged in observation missions often face severe pointing constraints, which can limit the ability of their solar panels optimize the generation of electrical power during payload operations. Some approaches to maximize power generation involve either using orientable solar panels, or performing orbital maneuvers to point fixed or deployable (non-orientable) solar panels towards the Sun, which can compromise the payload's ability to meet its pointing requirements. In this study, an alternative approach that enables high-consuming remote sensing payloads to operate for extended periods without requiring orientable solar panels is presented. The satellite under consideration is equipped with fixed or deployable (non-orientable) solar panels mounted on up to three different perpendicular faces. The on-board payload is required to remain within a fixed angle from a target direction, which can be either nadir or a specific geographical position. To derive an optimal tracking law, the projected solar array area at each instant is maximized, resulting in optimal electrical power generation. This methodology allows the satellite to maintain its pointing constraints and efficiently operate high-consuming payloads for longer periods. The proposed tracking law is validated using an actual mission scenario, comparing the power generation achieved by the proposed method against other techniques. The approach presented in this paper shows improved performance, enabling satellites equipped with non-orientable solar panels to fulfill their observation requirements while optimizing electrical power generation. This work offers significant benefits for satellite operators, reducing the need for costly orientable solar panels and enhancing the overall efficiency of satellite missions. • Guidance law for optimal power generation while complying with payload pointing constraints. • Simple and easy formulation, suitable for real-time systems. • Methodology validated and compared against other attitude strategies. • Integration and simulation of the guidance law into a control scheme. [ABSTRACT FROM AUTHOR]

Change detection in high-resolution Earth observation is a fundamental Earth vision task to understand the subtle temporal dynamics of Earth's surface, significantly promoted by generic vision technologies in recent years. Vision Transformer is a powerful component to learning spatiotemporal representation but with enormous computation complexity, especially for high-resolution images. Besides, there is still lacking principles in designing macro architectures integrating these advanced vision components for various change detection tasks. In this paper, we present a deep probabilistic change model (DPCM) to provide a unified, interpretable, modular probabilistic change process modeling to address multiple change detection tasks, including binary change detection, one-to-many semantic change detection, and many-to-many semantic change detection. DPCM describes any complex change process as a probabilistic graphical model to provide theoretical evidence for macro architecture design and generic change detection task modeling. We refer to this probabilistic graphical model as the probabilistic change model (PCM), where DPCM is the PCM parameterized by deep neural networks. For parameterization, the PCM is factorized into many easy-to-solve distributions based on task-specific assumptions, and then we can use deep neural modules to parameterize these distributions to solve the change detection problem uniformly. In this way, DPCM has both theoretical macro architecture from PCM and strong representation capability of deep networks. We also present the sparse change Transformer for better parameterization. Inspired by domain knowledge, i.e., the sparsity of change and the local correlation of change, the sparse change Transformer computes self-attention within change regions to model spatiotemporal correlations, which has a quadratic computational complexity of the change region size but independent of image size, significantly reducing computation overhead for high-resolution image change detection. We refer to this instance of DPCM with sparse change Transformer as ChangeSparse to demonstrate their effectiveness. The experiments confirm ChangeSparse's superiority in speed and accuracy for multiple real-world application scenarios, such as disaster response and urban development monitoring. The code is available at https://github.com/Z-Zheng/pytorch-change-models. More resources can be found in http://rsidea.whu.edu.cn/resource_sharing.htm. [ABSTRACT FROM AUTHOR]

• The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. • HY-1C has the most effective extraction, followed by Landsat 8. • Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. This paper presents the extraction and classification of sea ice in the Yellow and Bohai seas using multi-source remote sensing products, including Landsat 8, HY-1C, and MODIS. We propose a Dynamic Thresholding Fully Automated (DTFA) method for sea ice extraction and classification. The accuracy of sea ice extraction and classification is evaluated using four metrics: confusion matrix accuracy rate, overall accuracy, user accuracy, and kappa coefficient. The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. HY-1C has the most effective extraction, followed by Landsat 8. Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. Finally, the fusion of multi-source remote sensing images was used to obtain the sea ice extent of the entire Chinese offshore. This method is also applicable to remote sensing monitoring of sea ice in other sea areas. [ABSTRACT FROM AUTHOR]

This paper focuses on the first human settlement of the Kohgiluyeh region in southwestern Iran in relation to regional environmental conditions. At an altitude between 500 and 3500 m asl, a range of different ecozones and diversified resources available at short distance compose this highly mountainous region. The first sedentary occupation occurred here during the 8th millennium BC. Applying remote sensing, GIS and geo-topographic landscape analyses to contextualize the available dataset, our study illustrates that in the southern portion of the region fertile lands played a pivotal role for the first settled communities and the stability of their subsistence strategies. Farming became even more important during the later Neolithic phase. Furthermore, Neolithic sites are recorded along the bottom of open slopes, at locations with direct access to the combined resources of the plains and the hilly lands. The first occupation of the region is also documented at high elevation up to 1600 m asl, along narrow valley formations, where limited areas are available for farming. [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]

This paper represents a wide incident angle-polarization insensitive, stable in results, and compact-sized metamaterial absorber that can be used for Doppler navigation aids and remote sensing applications in Earth Exploration Satellite Services. The unit cell dimension was subwavelength-sized of 0.112λ X 0.112λ, with a good effective medium ratio (8.9), and achieved polarization insensitivity at a maximum incident angle up to 180°, making it more effective for proposed applications. The absorber resonated at 3.855 GHz, 5.13 GHz, 9.855 GHz, 13.335 GHz, and 16.02 GHz with single negative metamaterial properties due to negative permeability and absorptions of 99.7 %, 99.2 %, 98.6 %, 98.1 %, and 97.9 %, respectively. An RLC equivalent circuit on ADS was analyzed to validate the simulated results using the dipoles created by the surface currents. The absorber is cost-effective as no lumped elements or multilayer substrate materials were used, making fabricating less time-consuming and simple. The simulated results were validated by measuring data from a vector network analyzer after fabricating it. The proposed metamaterial absorber is suitable for use in Doppler navigation and Earth Exploration Satellite Services, and this research can further be extended to beamforming and sensing applications in the future. [Display omitted] • A novel maze-enclosed quad symmetric kite-shaped metamaterial absorber is proposed for quad band applications. • The absorber was designed with subwavelength size of 0.112λ X 0.112λ mm with maximum absorption of 99.7%. • It works perfectly for both co and cross-polarized waves and was justified by simplified equivalent circuit. • Absorptions with SNG features can be used to improve sensing, antenna gain, and EM coupling reduction. • This absorber is ideal for using with Earth Exploration Satellite Services and Doppler Navigation systems. [ABSTRACT FROM AUTHOR]

Geospatial Data Analytics (GDA) is a futuristic platform for analyzing and processing volumetric data in remote sensing and GIS applications. GDA utilizes the Internet of Spatial Things (IoST), mist, fog, and cloud computing architecture as a backend tool for analyzing and processing big geospatial data. This paper introduces utilizing these interconnected network architectures of cloud, fog, and mist to process a large volume of geospatial data. Also, the paper presents a flexible, interconnected distributed network system, i.e., IoST-mist-fog-cloud GIS architecture, to analyze and manage geospatial data. The proposed system helps cloud platforms when MIST devices are trying to cut down on latency and boost throughput at the edge of the IoST tier. It also performs the geospatial crime data visualization of the total number of stolen vehicles from 2001 to 2011 from all the states of India as a case study by using the proposed model. It explains the mathematical and analytical queueing model of the proposed system. In addition, it performs a performance evaluation and experimental findings on the proposed architecture and uses graphs to represent the various arithmetic outcomes. The experimentation result proves the proposed interconnected network architecture's efficacy in terms of reliability and efficiency. [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]

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]

To improve the analysis effect of the simulation and prediction of the expansion effect of urban construction land, this paper combines the digital sensing feature recognition and remote sensing analysis technology for the earth's surface, and uses Artificial-neural-network-based cellular automaton (ANN-CA) Markov model and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to study and analyze the expansion effect and trend of urban construction land, and subdivides the spatial and temporal characteristics of land cover. In addition to that, multi-based sensing data is used to generate urban construction land expansion effect drivers. The multi-source sensing data and InVEST model are used to conduct spatial information analysis and ecological early-warning. The research shows that combining digital sensing feature identification and remote sensing analysis technology, using the analysis model of ANN-CA-Markov model and InVEST model can effectively improve the analysis effect of urban construction land expansion effect prediction, and carry out ecological early-warning of construction land expansion on this basis. Overall territorial spatial planning stage of Xuzhou city, the designated ecological space area accounts for 20% of the whole land area. There is a sharp contradiction between constructive land expansion and ecological security in the whole region, so it is appropriate to conduct precise intervention and efficient management of constructive land expansion according to ecological early-warning. • Improving the simulation of urban construction land expansion by sensing technology. • Using Digital sensing land cover feature recognition based on multi-source sensing data. • The erosion of plough was most affected by the driver factor. • ANN-CA-Markov model to improve the accuracy of simulation. • Measurement models combined with remote sensing technology for the earth. [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]

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]

Soil moisture is an important physical quantity that reflects the surface conditions. Soil moisture retrieval from remote sensing satellite monitoring data is a common method at present and the crucial issue is how to eliminate the influence of other surface and reflect soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic wave. In this paper, the Sentinel-1 SAR data, optical data and other auxiliary data such as the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration are used as data sources. By coupling the Advanced Integral equation (AIEM) model, a method suitable for the inversion of soil moisture content in the Duolun area is established, and the inverted soil moisture map is analyzed. [ABSTRACT FROM AUTHOR]

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]

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [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]

[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]

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]

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]

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]

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]

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]

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]

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]

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]

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]