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This document, titled "Computational Intelligence in Remote Sensing," discusses the application of computational intelligence (CI) methods in the field of remote sensing. It highlights recent research and progress in this area, categorizing the papers into four sections: computational intelligence methods in hyperspectral remote sensing images, object detection techniques in remote sensing images, deep learning approaches in remote sensing image classification, and intelligent optimization and control in satellite image applications. The document emphasizes the potential of CI in addressing the challenges of remote sensing and encourages further interdisciplinary cooperation to solve real-world problems. The authors express their gratitude to the contributors and highlight the achievements of the research papers in this journal. [Extracted from the article]

Accurate and unified information from the increasingly remote sensing (RS) scenes is important for RS applications in multi-sectoral association services of natural resource management. However, these applications in mountain areas are limited by the challenging issues of random geometric distortions and erroneous spatial associations. The paper introduces digital elevation model (DEM) maps as a unified geographic reference to search and match homonymy ground points (HGPs). The proposed computer-based procedure was tested with Landsat TM, ETM and HJ-1B satellite images using ASTER global DEM in the Longitudinal Rang-gorge Valley Region of Southwest China. 1322, 3551 and 694 pairs of HGPs were identified and acquired the geometric accuracies with 43 m (TM), 14 m (ETM) and 123 m (HJ), respectively. The deviations are significantly reduced and the disjoint ground objects are matched. The study satisfies the application requirement of multispectral satellite imagery with less labour and time costs. [ABSTRACT FROM AUTHOR]

Satellite imagery has become a major source for identifying and mapping terrestrial and planetary landforms. However, interpretating landforms and their significance, especially in changing environments, may still be questionable. Consequently, ground truth to check training models, especially in mountainous areas, can be problematic. This paper outlines a decimal format, [dLL], for latitude and longitude geolocation that can be used for model interpretation and validation and in data sets. As data have positions in space and time, [dLL] defined points, as for images, can be associated with metadata as nodes. Together with vertices, metadata nodes help build 'information surfaces' as part of the Digital Earth. This paper examines aspects of the Critical Zone and data integration via the FAIR data principles, data that are; findable, accessible, interoperable and re-usable. Mapping and making inventories of rock glacier landforms are examined in the context of their geomorphic and environmental significance and the need for geolocated ground truth. Terrestrial examination of rock glaciers shows them to be predominantly glacier-derived landforms and not indicators of permafrost. Remote-sensing technologies used to track developing rock glacier surface features show them to be climatically melting glaciers beneath rock debris covers. Distinguishing between glaciers, debris-covered glaciers and rock glaciers over time is a challenge for new remote sensing satellites and technologies and shows the necessity for a common geolocation format to report many Earth surface features. [ABSTRACT FROM AUTHOR]

In complex environments a single visible image is not good enough to perceive the environment, this paper proposes a novel dual-stream real-time detector designed for target detection in extreme environments such as nighttime and fog, which is able to efficiently utilise both visible and infrared images to achieve Fast All-Weatherenvironment sensing (FAWDet). Firstly, in order to allow the network to process information from different modalities simultaneously, this paper expands the state-of-the-art end-to-end detector YOLOv8, the backbone is expanded in parallel as a dual stream. Then, for purpose of avoid information loss in the process of network deepening, a cross-modal feature enhancement module is designed in this study, which enhances each modal feature by cross-modal attention mechanisms, thus effectively avoiding information loss and improving the detection capability of small targets. In addition, for the significant differences between modal features, this paper proposes a three-stage fusion strategy to optimise the feature integration through the fusion of spatial, channel and overall dimensions. It is worth mentioning that the cross-modal feature fusion module adopts an end-to-end training approach. Extensive experiments on two datasets validate that the proposed method achieves state-of-the-art performance in detecting small targets. The cross-modal real-time detector in this study not only demonstrates excellent stability and robust detection performance, but also provides a new solution for target detection techniques in extreme environments. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds. [ABSTRACT FROM AUTHOR]

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy. [ABSTRACT FROM AUTHOR]

Remote sensing applications have a long history in treeline research. Recent reviews have examined the topic mainly from a methodological point of view. Here, we propose a question‐oriented review of remote sensing in treeline ecology to relate remote sensing methodologies to key ecological metrics and identify knowledge gaps and promising areas for future research. We performed a meta‐analysis to assess the role of remote sensing as a tool for measuring spatial patterns and dynamics of alpine and Arctic treeline ecotone globally. We assessed the geographic distribution, scale of analysis, and relationships between remote sensing techniques and treeline ecological metrics through co‐occurrence mapping and multivariate statistics. Our analysis revealed that only 10% of treeline ecology studies applied remote sensing tools, often associated with the keyword 'climate change'. Monitoring studies adopted coarser spatial resolutions over longer temporal extents in comparison with other treeline studies. A multiscale and multi‐sensor spatial approach was implemented in just 19% of papers. Long‐term research commonly relied on aerial and oblique photography to measure treeline shifts through photointerpretation within a multidisciplinary framework. More recent treeline dynamics were often quantified using greenness trends derived from the pixel‐based classification of satellite images. Many recent short‐term studies focused on delineating tree scale metrics derived from the object‐based classification of uncrewed aerial vehicle (UAV) images or LiDAR data. Over the past decade, high‐resolution and low‐cost UAV remote sensing has emerged as an interesting opportunity to fill the gap between local‐scale ecological patterns and coarse‐resolution satellite sensors. Additionally, treeline remote sensing applications would strongly benefit from multidisciplinary frameworks that integrate field studies in ecology and environmental science. The multi‐dimensional structural complexity of treelines typically responds to environmental drivers over multiple scales and thus is best described with multiscale and multi‐sensor approaches. [ABSTRACT FROM AUTHOR]

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, "different objects with the same spectrum" or "the same object with different spectra", and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. [ABSTRACT FROM AUTHOR]

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining realtime performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network's ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network's generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity. [ABSTRACT FROM AUTHOR]

With the rapid development of the energy industry and the growth of the global energy demand in recent years, the development of the photovoltaic industry has become increasingly significant. However, the development of the PV industry is constrained by high land costs, and land in central cities and industrial areas is often very expensive and unsuitable for the installation of PV equipment in large areas. With this background knowledge, the key to evaluating the PV potential is by counting the rooftop information of buildings, and an ideal solution for extracting building rooftop information is from remote sensing satellite images using the deep learning method; however, the deep learning method often requires large-scale labeled samples, and the labeling of remote sensing images is often time-consuming and expensive. To reduce the burden of data labeling, models trained on large datasets can be used as pre-trained models (e.g., ImageNet) to provide prior knowledge for training. However, most of the existing pre-trained model parameters are not suitable for direct transfer to remote sensing tasks. In this paper, we design a pseudo-label-guided self-supervised learning (PGSSL) semantic segmentation network structure based on high-resolution remote sensing images to extract building information. The pseudo-label-guided learning method allows the feature results extracted by the pretext task to be more applicable to the target task and ultimately improves segmentation accuracy. Our proposed method achieves better results than current contrastive learning methods in most experiments and uses only about 20–50% of the labeled data to achieve comparable performance with random initialization. In addition, a more accurate statistical method for building density distribution is designed based on the semantic segmentation results. This method addresses the last step of the extraction results oriented to the PV potential assessment, and this paper is validated in Beijing, China, to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Persistent Scatterer Interferometry (PSI) is a remote sensing measurement technology based on electromagnetic waves, capable of simultaneously monitoring deformations in large urban building complexes with millimeter‐level precision. However, relying solely on the line of sight (LOS) deformation sequence based on a specific permanent scatterer cannot accurately analyze building deformations, particularly in cases where high‐rise buildings may simultaneously experience various deformation components such as temperature‐induced deformation, shrinkage, creep, and tilting. To accurately identify the deformation states of high‐rise buildings, the paper begins by systematically summarizing three typical deformation patterns from the unique perspective of synthetic aperture radar (SAR) satellites. These patterns include Pattern I, characterized by temperature‐induced deformation alone, and Pattern II and Pattern III, which involve a combination of deformation in different directions relative to the SAR satellite in addition to temperature‐induced deformation. To accurately monitor the LOS deformation of high‐rise buildings, the paper introduces the concept of acquiring the evolutionary trends of temperature‐related deformation coefficients and proposes a methodology for recognizing and quantifying deformation in high‐rise buildings. Subsequently, this study utilized freely available Sentinel‐1 satellite data to observe the deformation of nine high‐rise buildings in Changsha, China. The research findings indicate that the thermal expansion coefficients of most high‐rise buildings fall within the range of 6 ∼ 12 × 10−6/°C. High‐rise buildings that have been constructed for more than ten years almost no longer experience significant shrinkage or creep, while new constructions may exhibit an initial shrinkage and creep of up to 1.2 × 10−4 mm/mm. Additionally, the study results demonstrate that super‐tall buildings may exhibit centimeter‐scale lateral deformations at their tops due to uneven shrinkage. Findings from the study indicate that the proposed method can achieve cost‐effective and sustainable deformation monitoring of high‐rise building clusters within a large urban area. [ABSTRACT FROM AUTHOR]

Mussel platforms are big floating structures made of wood (normally about 20 m × 20 m or even a bit larger) that are used for aquaculture. They are used for supporting the growth of mussels in suitable marine waters. These structures are very common near the Galician coastline. For their maintenance and tracking, it is quite convenient to be able to produce a periodic census of these structures, including their current count and position. Images from Earth observation satellites are, a priori, a convenient choice for this purpose. This paper describes an application capable of automatically supporting such a census using optical images taken at different wavelength intervals. The images are captured by the two Sentinel 2 satellites (Sentinel 2A and Sentinel 2B, both from the Copernicus Project). The Copernicus satellites are run by the European Space Agency, and the produced images are freely distributed on the Internet. Sentinel 2 images include thirteen frequency bands and are updated every five days. In our proposal, remote-sensing normalized (differential) indexes are used, and machine-learning techniques are applied to multiband data. Different methods are described and tested. The results obtained in this paper are satisfactory and prove the approach is suitable for the intended purpose. In conclusion, it is worth noting that artificial neural networks turn out to be particularly good for this problem, even with a moderate level of complexity in their design. The developed methodology can be easily re-used and adapted for similar marine environments. [ABSTRACT FROM AUTHOR]

Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target detection model called RN-YOLO (YOLO with RepGhost and NAM), which integrates RepGhost and a normalization-based attention module (NAM) based on YOLOv8. Firstly, NAM is added to the feature extraction network to enhance the capture capabilities for small targets by recalibrating receptive fields and strengthening information flow. Secondly, an efficient RepGhost_C2f structure is employed in the feature fusion network to replace the C2f module, effectively reducing the parameters. Lastly, the WIoU (Wise Intersection over Union) loss function is adopted to mitigate issues such as significant variations in target sizes and difficulty locating small targets, effectively improving the localization accuracy of small targets. The experimental results demonstrate that compared to the YOLOv8s model, the RN-YOLO model reduces the parameter count by 13.9%. Moreover, on the DOTAv1.5, TGRS-HRRSD, and RSOD datasets, the detection accuracy (mAP@.5:.95) of the RN-YOLO model improves by 3.6%, 1.2%, and 2%, respectively, compared to the YOLOv8s model, showcasing its outstanding performance and enhanced capability in detecting small targets. [ABSTRACT FROM AUTHOR]

Existing deep learning-based change detection networks encounter challenges related to the temporal dependency inherent in dual-temporal images. In this study, a weight-shared dual-difference change detection network (DDCDNet) model is proposed based on feature extraction networks. The model employs feature discrimination modules fused with spatial and channel attention mechanisms at different hierarchical levels of the backbone network. In the encoding phase, the tiny version of the Swin Transformer is utilized as the backbone network, with a weightsharing strategy applied to extract feature information from bitemporal remote sensing images. The proposed model in this paper is experimented on the LEVIR-CDþand DSIFN datasets, achieving F1-scores (F1) of 87.71% and 85.79%, recalls of 83.87% and 81.17%, and IoU (Intersection over Union) scores of 78.11% and 75.12%, respectively. These results indicate that the proposed model significantly outperforms other comparative models, demonstrating a better capability of identifying temporal changes in buildings, excellent generalization capability. [ABSTRACT FROM AUTHOR]

Semantic segmentation stands as a prominent domain within remote sensing that is currently garnering significant attention. This paper introduces a pioneering semantic segmentation model based on TransUNet architecture with improved coordinate attention for remote-sensing imagery. It is composed of an encoding stage and a decoding stage. Notably, an enhanced and improved coordinate attention module is employed by integrating two pooling methods to generate weights. Subsequently, the feature map undergoes reweighting to accentuate foreground information and suppress background information. To address the issue of time complexity, this paper introduces an improvement to the transformer model by sparsifying the attention matrix. This reduces the computing expense of calculating attention, making the model more efficient. Additionally, the paper uses a combined loss function that is designed to enhance the training performance of the model. The experimental results conducted on three public datasets manifest the efficiency of the proposed method. The results indicate that it excels in delivering outstanding performance for semantic segmentation tasks pertaining to remote-sensing images. [ABSTRACT FROM AUTHOR]

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR2 = 0.01~0.27, ΔRMSE = 1.88~13.77 m3·hm−2, ΔMAE = 1.17~13.57 m3·hm−2). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R2 is greater than 0.91, while the fitting R2 of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the "airborne LiDAR + satellite remote sensing" data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project. [ABSTRACT FROM AUTHOR]

Mangroves are ecosystems that grow in the intertidal areas of coastal zones, playing crucial ecological roles and possessing unique economic and social values. They have garnered significant attention and research interest. Semantic segmentation of mangroves is a fundamental step for further investigations. However, mangrove remote sensing images often have large dimensions, with a substantial portion of the image containing mangrove features. Deep learning convolutional kernels may lead to inadequate receptive fields for accurate mangrove recognition. In mangrove remote sensing images, various challenges arise, including the presence of small and intricate details aside from the mangrove regions, which intensify the segmentation complexity. To address these issues, this paper primarily focuses on two key aspects: first, the exploration of methods to achieve a large receptive field, and second, the fusion of multi-scale information. To this end, we propose the Multi-Scale Fusion Attention Network (MSFANet), which incorporates a multi-scale network structure with a large receptive field for feature fusion. We emphasize preserving spatial information by integrating spatial data across different scales, employing separable convolutions to reduce computational complexity. Additionally, we introduce an Attention Fusion Module (AFM). This module helps mitigate the influence of irrelevant information and enhances segmentation quality. To retain more semantic information, this paper introduces a dual channel approach for information extraction through the deep structure of ResNet. We fuse features using the Feature Fusion Module (FFM) to combine both semantic and spatial information for the final output, further enhancing segmentation accuracy. In this study, a total of 230 images with dimensions of 768 pixels in width and height were selected for this experiment, with 184 images used for training and 46 images for validation. Experimental results demonstrate that our proposed method achieves excellent segmentation results on a small sample dataset of remote-sensing images, with significant practical value. This paper primarily focuses on three key aspects: the generation of mangrove datasets, the preprocessing of mangrove data, and the design and training of models. The primary contribution of this paper lies in the development of an effective approach for multi-scale information fusion and advanced feature preservation, providing a novel solution for mangrove remote sensing image segmentation tasks. The best Mean Intersection over Union (MIoU) achieved on the mangrove dataset is 86%, surpassing other existing models by a significant margin. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient and high-fidelity image fusion method based on adaptive smoothing filtering for panchromatic (PAN) and multispectral (MS) image fusion. The scale ratio reflects the ratio of spatial resolution between the panchromatic image and the multispectral image. When facing a multiscale fusion task, traditional methods are unable to simultaneously handle the problems of spectral resolution loss resulting from high scale ratios and the issue of reduced spatial resolution due to low scale ratios. To adapt to the fusion of panchromatic and multispectral satellite images of different scales, this paper improves the problem of the insufficient filtering of high-frequency information of remote sensing images of different scales by the classic filter-based intensity modulation (SFIM) model. It uses Gaussian convolution kernels instead of traditional mean convolution kernels and builds a Gaussian pyramid to adaptively construct convolution kernels of different scales to filter out high-frequency information of high-resolution images. It can adaptively process panchromatic multispectral images of different scales, iteratively filter the spatial information in panchromatic images, and ensure that the scale transformation is consistent with the definition of multispectral images. Using 15 common fusion methods, this paper compares the experimental results of ZY-3 with scale ratio 2.7 and SV-1 with scale ratio 4 data. The results show that the method proposed in this paper retains good spatial information for image fusion at different scales and has good spectral preservation. [ABSTRACT FROM AUTHOR]

Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape. [ABSTRACT FROM AUTHOR]

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects. [ABSTRACT FROM AUTHOR]

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter. [ABSTRACT FROM AUTHOR]

Forest Stock Volume (FSV) is one of the key indicators in forestry resource investigation and management on local, regional, and national scales. Limited by the saturation problems of optical satellite remote-sensing imagery in the retrieving of stock volume, and the high cost of Light Detection And Ranging (LiDAR) data, it is still challenging to estimate FSV in a large area using single-sensor remote-sensing data. In this paper, a method integrated multispectral satellite imagery and LiDAR data was developed to map stock volume in a large area. A random forest model was adopted to estimate the stock volume of larch forest in China based on the training samples from the Airborne Laser Scanning (ALS)-derived stock volume and corresponding Sentinel-2 imagery. Validation using National Forest Inventory (NFI) data, ALS-derived stock volume and ground investigation data demonstrated that the estimated stock volume had a high accuracy (R2 = 0.59, RMSE = 59.69 m3/ha, MD = 39.96 m3/ha when validated with NFI data; R2 ranged from 0.77 to 0.85, RMSE ranged from 38.68 m3/ha to 67.38 m3/ha, MD ranged from 24.90 m3/ha to 37.27 m3/ha when validated with ALS stock volume; R2 = 0.42, RMSE = 79.10 m3/ha, MD = 62.06 m3/ha when validated with field investigation data). Results of this paper indicated the applicability of estimating stock volume of larch forest in a large area by combining Sentinel-2 data and airborne LiDAR data. [ABSTRACT FROM AUTHOR]

Due to the limited semantic information extraction with small objects and difficulty in distinguishing similar targets, it brings great challenges to target detection in remote sensing scenarios, which results in poor detection performance. This paper proposes an improved YOLOv5 remote sensing image target detection algorithm, SEB-YOLO (SPD-Conv + ECSPP + Bi-FPN + YOLOv5). Firstly, the space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer module (SPD-Conv) was used to reconstruct the backbone network, which retained the global features and reduced the feature loss. Meanwhile, the pooling module with the attention mechanism of the final layer of the backbone network was designed to help the network better identify and locate the target. Furthermore, a bidirectional feature pyramid network (Bi-FPN) with bilinear interpolation upsampling was added to improve bidirectional cross-scale connection and weighted feature fusion. Finally, the decoupled head is introduced to enhance the model convergence and solve the contradiction between the classification task and the regression task. Experimental results on NWPU VHR-10 and RSOD datasets show that the mAP of the proposed algorithm reaches 93.5% and 93.9%respectively, which is 4.0% and 5.3% higher than that of the original YOLOv5l algorithm. The proposed algorithm achieves better detection results for complex remote sensing images. [ABSTRACT FROM AUTHOR]

Multispectral image registration is one of the most critical requirements to achieve reliable remote sensing goals such as change detection, image fusion, etc., due to providing complementary knowledge of the scene. On the one hand, this issue has always been a hot topic of research according to significant appearance differences, including geometric and nonlinear radiometric distortions. On the other hand, developing deep learning methods promises precise results in image processing and, in particular, image registration. It is no longer limited to low-level information structures, such as intensity and gradients. However, it is possible to provide more reliable results by extracting various high-level features and removing feature engineering. Therefore, we need extensive experiments in multispectral image registration to determine an efficient and robust method. To this end, this paper evaluates six well-known recently proposed learning-based feature descriptors, including LOFTR, TFeat, HardNet8, HardNet, SosNet, and HyNet, against geometric distortions within real multispectral images. Evaluations demonstrate the general superiority of the HardNet8 descriptor due to extracting high-level features within eight convolution layers. [ABSTRACT FROM AUTHOR]

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns. [ABSTRACT FROM AUTHOR]

High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2–7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder–decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model. [ABSTRACT FROM AUTHOR]

In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although models are often presented without proper realistic validation.This paper compares methods of monitoring dead tree areas using three data sources: multispectral aerial imagery, multispectral PlanetScope satellite imagery, and multispectral Sentinel-2 imagery, as well as two processing methods. The classification methods used are Random Forest (RF) and neural network (NN) in two modalities: pixel- and object-based. In total, 12 combinations are presented. The results were evaluated using two types of reference data: accuracy of model on validation data and accuracy on vector-format semi-automatic classification polygons created by a human evaluator, referred to as real Ground Truth. The aerial imagery was found to have the highest model accuracy, with the CNN model achieving up to 98% with object classification. A higher classification accuracy for satellite imagery was achieved by combining pixel classification and the RF model (87% accuracy for Sentinel-2). For PlanetScope Imagery, the best result was 89%, using a combination of CNN and object-based classifications. A comparison with the Ground Truth showed a decrease in the classification accuracy of the aerial imagery to 89% and the classification accuracy of the satellite imagery to around 70%. In conclusion, aerial imagery is the most effective tool for monitoring bark beetle calamity in terms of precision and accuracy, but satellite imagery has the advantage of fast availability and shorter data processing time, together with larger coverage areas. [ABSTRACT FROM AUTHOR]

The quantitative extraction and evolution stage identification of the Nitraria tangutorum nebkhas are the basis for the restoration of regional plants and the reconstruction of degraded ecosystems. In this paper, the Nitraria tangutorum nebkha in Dengkou County of China was taken as the research object. Through the spectral and texture information of Gaofen-2 satellite image, the quantitative extraction of Nitraria tangutorum nebkha area and coverage information was completed using methods of gray threshold method, mathematical morphology, FCLSU mixed pixel decomposition, kernel density spatial analysis; the current evolution stage of the Nitraria tangutorum nebkha was identified, and their spatial distribution characteristics were analyzed. The results showed that: (1) The user accuracy and mapping accuracy of Nitraria tangutorum nebkha extracted from Random Forest combined with object-oriented classification method were up to 90.32%. (2) The method proposed can achieve an accuracy of 93.76% in extracting the spatial position of Nitraria tangutorum nebkhas. (3) The evolution of Nitraria tangutorum nebkhas can be divided into three stages: embryonic or developmental stage, stable stage, and declining stage, with a proportion of 60.70%, 20.97%, and 18.33%, respectively; The Nitraria tangutorum nebkhas in the study area is mainly in their embryonic or developmental stage, and the proportion of Nitraria tangutorum nebkhas in the declining stage is also large. It can provide technical and theoretical support for the precise extraction of nebkhas in arid and semi-arid desert areas, the identification of their current evolutionary stages, and the study of their spatial distribution patterns. [ABSTRACT FROM AUTHOR]

Object detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images often have complex backgrounds and small objects, which results in a highly unbalanced distribution of foreground and complex background information. In order to solve the above problems, this paper proposes a multi-head channel and spatial trans-attention (MCSTA) module, which performs remote pixel interaction from the channel and spatial dimensions respectively to complete the attention feature capture function. It is a plug-and-play module that can be easily embedded in any other natural image object detection convolutional neural network, making it quickly applicable to remote sensing images. First, in order to reduce computational complexity and improve feature richness, we use a special linear convolution to obtain three projection features instead of the simple matrix multiplication transformation in Transformer. Second, we obtain trans-attention maps in different dimensions in a manner similar to the self-attention mechanism to capture the interrelationships of features in channels and spaces. In this process, we use a multi-head mechanism to perform parallel operations to improve speed. Furthermore, in order to avoid large-scale matrix operations, we specially designed an attention blocking mode to reduce computer memory usage and increase operation speed. Finally, we embedded the trans-attention module into YOLOv8, added a new detection head and optimized the feature fusion method, thus designing a lightweight small object detection model named TA-YOLO for remote sensing images. It has fewer parameters than the benchmark model YOLOv8, and its mAP on the PASCAL VOC and VisDrone data sets increased by 1.3% and 6.2% respectively. The experimental results prove the powerful function of the trans-attention module and the excellent performance of TA-YOLO. [ABSTRACT FROM AUTHOR]

Small landscape features (i.e., trees outside forest, small woody features) and linear vegetation such as hedgerows, riparian vegetation, and green lanes are vital ecological structures in agroecosystems, enhancing the biodiversity, landscape diversity, and protecting water bodies. Therefore, their monitoring is fundamental to assessing a specific territory's arrangement and verifying the effectiveness of strategies and financial measures activated at the local or European scale. The size of these elements and territorial distribution make their identification extremely complex without specific survey campaigns; in particular, remote monitoring requires data of considerable resolution and, therefore, is very costly. This paper proposes a methodology to map these features using a combination of open-source or low-cost high-resolution orthophotos (RGB), which are typically available to local administrators and are object-oriented classification methods. Additionally, multispectral satellite images from the Sentinel-2 platform were utilized to further characterize the identified elements. The produced map, compared with the other existing layers, provided better results than other maps at the European scale. Therefore, the developed method is highly effective for the remote and wide-scale assessment of SWFs, making it a crucial tool for defining and monitoring development policies in rural environments. [ABSTRACT FROM AUTHOR]

Cloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications. To mitigate the adverse impact of cloud coverage and recover ground information obscured by clouds, this paper presents a curvature-driven cloud removal method. Considering that each image can be regarded as a curved surface and the curvature can reflect the texture information well due to its dependence on the surface's undulation degree, the presented method transforms image from natural domain to curvature domain for information reconstruction to maintain details of reference image. In order to improve the overall consistency and continuity of cloud removal results, the optimal boundary for cloud coverage area replacement is determined first to make the boundary pass through pixels with minimum curvature difference. Then, the curvature of missing area is reconstructed based on the curvature of reference image, and the reconstructed curvature is inversely transformed to natural domain to obtain a cloud-free image. In addition, considering the possible significant radiometric differences between different images, the initial cloud-free result will be further refined based on specific checkpoints to improve the local accuracy. To evaluate the performance of the proposed method, both simulated experiments and real data experiments are carried out. Experimental results show that the proposed method can achieve satisfactory results in terms of radiometric accuracy and consistency. [ABSTRACT FROM AUTHOR]

Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly, to tackle the issues of foreground–background imbalance and scale variation, a module based on multi-dilated rate convolution fusion was integrated into a decoder. This module extended the receptive field through multi-dilated convolution, enhancing the model's capability to capture global features. Secondly, to address the diversity of scenes and background interference, a hybrid attention module based on large-kernel convolution was employed to improve the performance of the decoder. This module, based on a combination of spatial and channel attention mechanisms, enhanced the extraction of contextual information through large-kernel convolution. A convolution kernel selection mechanism was also introduced to dynamically select the convolution kernel of the appropriate receptive field, suppress irrelevant background information, and improve segmentation accuracy. Ablation studies on the Vaihingen and Potsdam datasets demonstrate that our decoder significantly outperforms the baseline in terms of mean intersection over union and mean F1 score, achieving an increase of up to 1.73% and 1.17%, respectively, compared with the baseline. In quantitative comparisons, the accuracy of our improved decoder also surpasses other algorithms in the majority of categories. The results of this paper indicate that our improved decoder achieves significant performance improvement compared with the old decoder in remote-sensing image-segmentation tasks, which verifies its application potential in the field of land-cover segmentation. [ABSTRACT FROM AUTHOR]

Wheat scab can reduce wheat yield and quality. Currently, unmanned aerial vehicles (UAVs) are widely used for monitoring field crops. However, UAV is constrained by limited computational resources on-board the platforms. In addition, compared to ground images, UAV images have complex backgrounds and smaller targets. Given the aforementioned challenges, this paper proposes a lightweight wheat scab detection network based on UAV. In addition, overlapping cropping and image contrast enhancement methods are designed to preprocess UAV remote-sensing images. Additionally, this work constructed a lightweight wheat scab detection network called LWSDNet using mixed deep convolution (MixConv) to monitor wheat scab in field environments. MixConv can significantly reduce the parameters of the LWSDNet network through depthwise convolution and pointwise convolution, and different sizes of kernels can extract rich scab features. In order to enable LWSDNet to extract more scab features, a scab feature enhancement module, which utilizes spatial attention and dilated convolution, is designed to improve the ability of the network to extract scab features. The MixConv adaptive feature fusion module is designed to accurately detect lesions of different sizes, fully utilizing the semantic and detailed information in the network to enable more accurate detection by LWSDNet. During the training process, a knowledge distillation strategy that integrates scab features and responses is employed to further improve the average precision of LWSDNet detection. Experimental results demonstrate that the average precision of LWSDNet in detecting wheat scab is 79.8%, which is higher than common object detection models and lightweight object detection models. The parameters of LWSDNet are only 3.2 million (M), generally lower than existing lightweight object detection networks. [ABSTRACT FROM AUTHOR]

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter), which combines deep semantic segmentation networks (DSSN) and GCNs. In the GCN module, a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships. A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship information in the original feature information. Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images, the DGCN significantly optimizes the segmentation effect of feature boundaries, effectively reduces the noise in the segmentation results and improves the segmentation accuracy, which demonstrates the advancements of our model. [ABSTRACT FROM AUTHOR]

To overcome the limitations of traditional on-orbit modulation function transfer (MTF) measurement methods that are heavily dependent on natural features, scenery, artificial edges, and point source targets, this paper presents an on-orbit MTF measurement method of remote sensing imager based on the refined image kernel (RIK) acquired directly from remote sensing images. First, the kernel is estimated from some remote sensing sub-images with rich texture details by using an iterative support detection (ISD) algorithm; then, it is refined by central pixel energy concentration (EC) to obtain the RIK. Secondly, the MTF curves are calculated by interpolating RIK and Fourier transform. Finally, the final MTF is the average value of MTFs at Nyquist frequency acquired by each RIK. To demonstrate the feasibility and validity of this method, the MTFs were compared to the result of the ISO12233 edge method with an error of no more than 7%. The relative error of the measured results does not exceed 5% for image signal-to-noise ratio (SNR) above 20dB. The results obtained from the on-orbit MTF measurement using remote sensing images of the Jilin-1 satellite have a maximum error of less than 2% compared with the ISO12233 edge method. These demonstrate that the method proposed in this paper supplies highly accurate and robust results and can successfully increase the efficiency of on-orbit MTF measurement, providing a reference for high-frequency monitoring of satellite on-orbit stability and their optical imaging quality. [ABSTRACT FROM AUTHOR]

For many remote sensing applications, the instantaneous waterline on the image is critical boundary information to separate land and water and for other purposes. Accurate waterline extraction from satellite images is a desirable feature in such applications. Due to the complex topography of low tidal flats and their indistinct spatial and spectral characteristics on satellite imagery, the waterline extraction for tidal flats (especially at low tides) from remote sensing images has always been a technically challenging problem. We developed a novel method to extract waterline from satellite images, assuming that the waterline's elevation is level. This paper explores the utilization of bathymetry during waterline extraction and presents a novel approach to tackle the waterline extraction issue, especially for low tidal flats, using remote sensing images at mid/high tide, when most of the tidal flat area is filled with seawater. Repeated optical satellite images are easily accessible in the current days; the proposed approach first generates the bathymetry map using the mid/high-tide satellite image, and then the initial waterline is extracted using traditional methods from the low-tide satellite image; the isobath (depth contour lines of bathymetry), which corresponds to the initial waterline is robustly estimated, and finally an area-based optimization algorithm is proposed and applied to both isobath and initial waterline to obtain the final optimized waterline. A series of experiments using Sentinel-2 multispectral images are conducted on Jibei Island of Penghu Archipelago and Chongming Island to demonstrate this proposed strategy. The results from the proposed approach are compared with the Normalized Difference Water Index (NDWI) and Support Vector Machine (SVM) methods. The results indicate that more accurate waterlines can be extracted using the proposed approach, and it is very suitable for waterline extraction for tidal flats, especially at low tides. [ABSTRACT FROM AUTHOR]

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm. [ABSTRACT FROM AUTHOR]

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets. [ABSTRACT FROM AUTHOR]

In this paper, a new simultaneous retrieval method of the SIRAW algorithm is introduced and carried out on FY3D/MERSI-II satellite images to obtain the aerosol optical thickness (AOT) and normalized water-leaving radiance (WLR) over the ocean. In order to improve the operation efficiency of SIRAW, a machine learning solver is developed to improve the speed of forward radiative transfer computation during retrieval. Ground-based measurement data from AERONET-OC and satellite products from VIIRS are used for comparative verification. The results show that the retrieved AOT and WLR from SIRAW are both in good agreement with those of AERONET-OC and VIIRS. Further, considering the degradation of the MERSI sensor, a new calibration scheme on 412 nm and 443 nm is adopted and an evaluation is carried out. Inter-comparison of derived WLR between MERSI and VIIRS indicates that the new calibration scheme could effectively improve the WLR retrieval accuracy of MERSI with better consistency to the official data of VIIRS. Therefore, this paper confirms that a simultaneous retrieval scheme combined with effective calibration coefficients can be used for high-precision retrieval of real aerosol and water-leaving radiation. [ABSTRACT FROM AUTHOR]

Guilin is situated in the southern part of China with abundant rainfall. There are 137 reservoirs, which are widely used for irrigation, flood control, water supply and power generation. However, there has been a lack of systematic and full-coverage remote sensing monitoring of reservoir dams for a long time. According to the latest public literature, high-resolution unmanned aerial vehicle (UAV) remote sensing has not been used to detect changes on the reservoir dams of Guilin. In this paper, an intelligent segmentation change detection method is proposed to complete the detection of dam change based on multitemporal high-resolution UAV remote sensing data. Firstly, an enhanced GrabCut that fuses the linear spectral clustering (LSC) superpixel mapping matrix and the Sobel edge operator is proposed to extract the features of reservoir dams. The edge operator is introduced into GrabCut to redefine the new energy function's smooth item, which makes the segmentation results of enhanced GrabCut more robust and accurate. Then, through image registration, the multitemporal dam extraction results are unified to the same coordinate system to complete the difference operation, and finally the dam change results are obtained. The experimental results of two representative reservoir dams in Guilin show that the proposed method can achieve a very high accuracy of change detection, which is an important reference for related research. [ABSTRACT FROM AUTHOR]

Detecting aircraft in remote sensing images poses a formidable challenge due to the diverse characteristics of aircraft, including type, size, pose, and intricate backgrounds. Traditional algorithms encounter difficulties in manually extracting features from numerous candidate regions. This paper introduces an innovative aircraft detection approach that combines corner clustering with a diverse set of Deep Learning (DL) models. The proposed method involves two main stages: region proposal and classification. In the region proposal stage, initial candidate regions are generated using a mean-shift clustering algorithm applied to corners detected on binary images. Subsequently, a comprehensive set of classifiers, encompassing CNN, DenseNet, MobileNetV2, Inception v3, Random Forest (R.F), ResNet50, ResNeXT, Support Vector Machine (SVM), VGG16, Xception, EfficientNet, and InceptionResNetv2, is employed for feature extraction and classification. The presented approach demonstrates superior accuracy and efficiency compared to conventional methods. By leveraging the autonomous learning capabilities of CNN and DL models on extensive datasets, the methodology generates a reduced yet high-quality set of candidate regions. Inspired by the detection methodology employed by image analysts, the approach adopts a coarse-to-fine strategy using CNN and DL models. The first CNN proposes coarse candidate regions, and the second identifies individual airplanes within these regions in finer detail. This framework results in a decreased number of candidate regions compared to existing literature while extracting distinctive deep features. Experimental evaluations on Google Earth images validate the efficiency of the proposed method, underscoring its potential for practical applications in both civilian and military contexts. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than 30 % and reduce the matching time by more than 40 % . This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images. [ABSTRACT FROM AUTHOR]

High-resolution remote-sensing images cover more feature information, including texture, structure, shape, and other geometric details, while the relationships among target features are more complex. These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features. [ABSTRACT FROM AUTHOR]

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, "resolution", "Landsat", "yield", "wheat" and "multispectral" are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors. [ABSTRACT FROM AUTHOR]