Remote Sensing Image Analysis Research Articles

Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification

The hyperspectral image (HSI) classification task is widely used in remote sensing image analysis. The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. However, they cannot well utilize the sequential properties of spectral features and face the challenge of increasing computational cost with the increase in network depth. To address these shortcomings, this paper proposes a novel network with a CNN-Mamba architecture, called DBMamba, which uses a bidirectional Mamba to process spectral feature sequences at a linear computational cost. In the DBMamba, principal component analysis (PCA) is first used to extract the main features of the data. Then, a dual-branch CNN structure, with the fused features from spectral–spatial features by 3D-CNN and spatial features by 2D-CNN, is used to extract shallow spectral–spatial features. Finally, a bidirectional Mamba is used to effectively capture global contextual information in features and significantly enhance the extraction of spectral features. Experimental results on the Indian Pines, Salinas, and Pavia University datasets demonstrate that the classification performance surpasses that of many cutting-edge methods, improving by 1.04%, 0.15%, and 0.09%, respectively, over the competing SSFTT method. The research in this paper enhances the existing knowledge on HSI classification and provides valuable insights for future research in this field.

Multi-Source Fusion Enhanced Feature Segmentation in Remote Sensing Imagery

Abstract. With deepening application of deep learning technology in the field of remote sensing, several challenges persist in the segmentation of remote sensing optical images. These challenges include: (1) insufficient availability of deep learning-based remote sensing semantic segmentation datasets; (2) inadequate utilization of multi-source remote sensing data in the field of semantic segmentation; (3) limited sample size for effective model training, as well as the need to enhance both the speed and accuracy of model training. To address these challenges, this study introduces a multi-source remote sensing dataset consisting of 15000 data pairs, each comprising remote sensing multispectral data, synthetic aperture radar(SAR) images, land use and land cover(LULC) data, digital elevation model (DEM) data as well as the analysis data including Slope, Aspect, and Hillshade. Through the application of an end-to-end network based on pix2pix, effective fusion and feature enhancement of multi-source remote sensing data were achieved. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and Spectral Angle Mapper(SAM) values reached 0.84, 23.14, and 0.19, respectively, representing significant improvements over the baseline Pix2pix model’s performance metrics of 0.62, 18.84, and 0.23. In downstream semantic segmentation applications, the enhanced dataset was utilized to train semantic segmentation models for remote sensing image analysis. This approach effectively improved the training speed and segmentation accuracy of the models, with the mean intersection over union (mIoU) increasing from 0.467 to 0.481 and accuracy rising from 0.734 to 0.746. Moreover, the visual representation of remote sensing image segmentation demonstrated noticeable enhancements.

Faulted karst reservoir spaces in Middle-Lower Triassic carbonates, Qingjiang Region, Yangtze block, China

The complexity and heterogeneity of ultra-deep faulted karst reservoirs pose significant challenges for hydrocarbon exploration. In this study, we integrated remote sensing image analysis, field measurements, and core sample testing to evaluate the characteristics and controlling factors of carbonate reservoir spaces within the Middle and Lower Triassic formations along a regional strike-slip fault in the Qingjiang region of China. The strike-slip fault zone is composed of multiple fault cores and damage zones. Based on differences in damage zone width and linear fault density, the fault zone was subdivided into transtensional and transpressional segmentations. The carbonate reservoirs, primarily developed within the damage zones, consist of fractures, fracture clusters, and cavities. Detailed measurements of the carbonate outcrops were conducted to obtain geometric parameters of the reservoir spaces. Quantitative results indicate that in the transtensional segmentation, the reservoir is dominated by tensile fracture-cavity systems, characterized by larger fracture apertures (0.13–1.25 m), higher linear fracture density (0.38–8.37 m⁻1), and well-developed cavities (0.03–4.84 m2), which contribute to better fluid connectivity and storage capacity. In contrast, the transpressional segmentation is dominated by compressional fracture-fracture cluster systems, with longer fractures (0.11–12.52 m), smaller fracture apertures (0.01–0.94 m), and extensive fracture clusters development (0.18–17.87 m2), but with lower fluid connectivity and limited storage capacity. Mechanical testing results show that the average compressive strength in the transtensional segmentation (133.95 MPa) is significantly higher than that in the transpressional segmentation (70.28 MPa). In terms of mineral composition, the transtensional segmentation has a higher calcite content, whereas the transpressional segmentation is richer in dolomite and quartz. Based on the observed differences in reservoir space characteristics across the strike-slip fault zone, we discussed the combined effects of structural segmentation, formation thickness, rock mechanics, and brittle mineral content on reservoir space development. The study emphasizes that stress conditions (primary factor) and material properties (secondary factor) jointly control fluid migration and storage efficiency in the reservoirs. Additionally, we suggest that the outcrop studies in the Qingjiang region provide valuable geological analogs for faulted karst reservoirs, offering critical insights for improving the precision of carbonate reservoir exploration and optimizing production efficiency.

Nested transformer decoder using dense skip-connections for change detection in high-resolution remote sensing image

ABSTRACT Change detection is a critical task in remote sensing image analysis, with applications in land management and urban development. Convolutional neural networks (CNNs) have shown success in this area, but existing CNN-based methods often struggle with irrelevant changes caused by interfering factors, leading to unsatisfactory results. To address this, some studies have replaced CNN layers with transformers to enhance the receptive field and utilize global information better. However, this can make models overly reliant on global information, potentially overlooking shallow, fine-grained features, especially at the edges of changed targets. To overcome these limitations, we propose a Nested Transformer Decoder (NTD) that combines the strengths of transformers and CNNs by introducing dense skip connections. While having a large receptive field, it can also better retain shallow and fine-grained features by adding skip-connection. These innovations enable our model to more accurately identify changes and preserve the edges of the changed areas, including small changes that are difficult to detect using existing methods. Additionally, we introduce a Residual Fusion Module (RFM) to reduce false detections caused by irrelevant factors like light and shadow. The RFM employs residual information and spatiotemporal attention to suppress differences in multi-scale features, leading to better detection outcomes. The experimental results demonstrate that our proposed model outperforms the current state-of-the-art change detection methods in various metrics, and represents a novel and effective solution to the challenge of change detection in remote sensing images. Experimental results show that our model surpasses state-of-the-art change detection methods across various metrics. Tested on three public change detection datasets, our method improves the Intersection over Union (IoU) for the change class by 2.83% to 16.39% and enhances the F1-score by 1.60% to 9.63%. These results demonstrate that the proposed approach provides an effective solution to the challenges of change detection in remote sensing images.

Monitoring of floating structures using segmentation and object classification of remotely-sensed images

Abstract With the rapid development of Marine aquaculture, remote sensing monitoring of high space separation Marine floating raft aquaculture has become a key technology to ensure aquaculture efficiency and environmental sustainability. A novel multi-scale layered cascade capsule network (MSHCN) model is proposed to improve the accuracy and efficiency of remote sensing image analysis for high space separation Marine floating raft culture. Firstly, based on the basic principle of the capsule network, the traditional convolutional neural network is improved, and a multi-scale feature extraction mechanism is introduced to adapt to the diversity and complexity of images in the culture area. Secondly, by building a hierarchical cascade structure, the model can further improve its ability to capture small and large scale features, so as to better understand the spatial distribution characteristics of the farming environment. In addition, in the experimental part, we verified with real remote sensing data sets, and the results showed that the MSHCN model outperformed the current mainstream methods on several key performance indicators, including classification accuracy, detection speed and model generalization ability. Finally, the potential value of this model in practical application and future optimization direction is discussed, which provides a new idea and tool for the development of remote sensing monitoring technology for high space separation floating raft culture.

Integrating machine learning algorithms and fuzzy AHP for assessing livelihood vulnerability in Southwestern Coastal Bangladesh

The people living in the coastal regions of Bangladesh rely significantly on nature for their livelihoods, which renders them sensitive to climate change. The aim of this study is to determine the key indicators which contribute more to livelihood vulnerability of disaster-prone Gabura union in southwestern coastal Bangladesh. To achieve this goal, three machine learning algorithms are employed for determining the key indicators of livelihood vulnerability. Subsequently, a livelihood vulnerability index (LVI) is constructed using these key indicators with the weighting of indicators facilitated by Fuzzy AHP method. And finally, a livelihood vulnerability map (LVM) is generated to visualize the spatial distribution and analysis of livelihood vulnerability within the union. The study employs a mixed-methods approach, including questionnaire surveys, focus group discussions, key informant interviews, and remote sensing image analysis. A household survey of 950 households tracked livelihood vulnerability using 25 indicators across three domains of vulnerability, e.g., exposure, sensitivity, and adaptive capacity. ArcGIS and Google Earth Engine facilitated spatial data analysis. The Gabura Union exposed high vulnerability (LVI 0.63), driven by elevated exposure (0.61) and sensitivity (0.59) and lower adaptive capacity (0.30). The Livelihood Vulnerability Map (LVM) illustrates vulnerability across wards of the union, emphasizing high vulnerability zones on the periphery of the union, along the Kopothakho and Kholpetua rivers while central part of the union shows a moderate vulnerability level. The study's novelty lies in effectively integrating multiple methods for livelihood vulnerability assessment. Policymakers should target interventions focusing on areas along the Kopothakho River, to enhance community resilience.

Extraction of Garlic in the North China Plain Using Multi-Feature Combinations from Active and Passive Time Series Data

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping.

A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjecture

A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjecture

Spatial distribution and regional tectonic significance of the newly discovered Kalawenguquan fault in the interior of the Tian Shan Mountain Range, Western China

Quantifying the geometrical and kinematic aspects of faults within the Tian Shan Mountain Range is crucial for investigating the tectonic deformation patterns in the region. Through remote sensing image analysis using GaoFen-7 (GF-7) data and field geological surveys, we determined the geometric distribution and fault properties of the Kalawenguquan fault. The newly discovered Kalawenguquan fault spans more than 400 km and mainly strikes in the NEE direction, with some localized segments in the NE direction. The fault plane dips southward at angles ranging from 55° to 85°. The Kalawenguquan fault is a recently discovered Holocene active fault in the Tian Shan and is a thrust and left-lateral strike-slip fault. Based on geological survey findings and sediment dating data from the late Quaternary, the Kalawenguquan fault has a vertical sliding rate of approximately 0.41 mm/a. The left-lateral strike-slip rate was calculated to be 0.6–1.4 mm/a. We detected that at least two paleoseismic events occurred on the fault since 5.4 ± 0.4 ka. In the range of 41°-45° latitude, 45 % of the crustal shortening in the Tian Shan is absorbed by the internal structure.

Nonreciprocal feedback induces migrating oblique and horizontal banded vegetation patterns in hyperarid landscapes

In hyperarid environments, vegetation is highly fragmented, with plant populations exhibiting non-random biphasic structures where regions of high biomass density are separated by bare soil. In the Atacama Desert of northern Chile, rainfall is virtually nonexistent, but fog pushed in from the interior sustains patches of vegetation in a barren environment. Tillandsia landbeckii, a plant with no functional roots, survives entirely on fog corridors as a water source. Their origin is attributed to interaction feedback among the ecosystem agents, which have different spatial scales, ultimately generating banded patterns as a self-organising response to resource scarcity. The interaction feedback between the plants can be nonreciprocal due to the fact that the fog flows in a well-defined direction. Using remote sensing analysis and mathematical modelling, we characterise the orientation angle of banded vegetation patterns with respect to fog direction and topographic slope gradient. We show that banded vegetation patterns can be either oblique or horizontal to the fog flow rather than topography. The initial and boundary conditions determine the type of the pattern. The bifurcation diagram for both patterns is established. The theoretical predictions are in agreement with observations from remote sensing image analysis.

Satellite Image Cloud Automatic Annotator with Uncertainty Estimation

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations.

Hyperspectral-multispectral image fusion using subspace decomposition and Elastic Net Regularization

ABSTRACT The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with L 1 and L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image’s structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application.

Radiation Feature Fusion Dual-Attention Cloud Segmentation Network

In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms.

OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion.

As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model's capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications.

MFMENet: multi-scale features mutual enhancement network for change detection in remote sensing images

ABSTRACT Change detection, an important task in remote sensing image analysis, has been extensively studied in recent years. However, change detection still faces problems such as difficulty in detecting small targets and incomplete edge detection. Furthermore, pseudo changes such as seasonal changes can also lead to many false detections. In response to these challenges, we propose the Multi-scale Features Mutual Enhancement Network (MFMENet), a simple yet efficient network. MFMENet maximizes feature utilization through mutual guidance and supplementation, enhancing the detection capabilities for small targets and edges. First, we use a lightweight feature extraction network to extract features, which mitigates the information loss caused by continuous downsampling of an overly deep network structure. Then, we design a Context Adaptive Interaction Module (CAIM) to realize the complementarity of feature information at different levels. This facilitates shallow features in gaining more semantic information and deep features in acquiring more texture information, thereby enhancing the model’s capability to capture more comprehensive edge features while effectively mitigating interference from pseudo changes. Finally, we introduce a Feature Aggregation Comparison Module (FACM), which uses a combination of aggregation and comparison methods to refine and enhance features. FACM can not only highlight the changed features but also retain more details, improving the model’s detection ability of small targets and edge details. The full utilization of features and effective mutual enhancement of information ensure the improvement of MFMENet’s performance in small target and edge detection. Extensive experiments on three publicly available datasets (LEVIR, DSIFN, and CDD) demonstrate that our approach achieves superior performance with fewer parameters compared to state-of-the-art methods in recent years. In comparison to these baseline methods, our proposed approach achieves improvements of 0.98%, 12.24%, and 2.03% in the IOU metric on the LEVIR, DSIFN, and CDD datasets, respectively, while utilizing only 1.1 M parameters.

Shadow Detection in Remote Sensing Images Based on Spectral Radiance Separability Enhancement.

Shadow detection is a basic task of remote sensing image analysis, but it is often seriously disturbed by vegetation, water bodies, and black objects. It is observed that vegetation and dark objects often show a dark look in visible bands but brighter in the near-infrared (NIR), and is also noticed that the reflection of inland water bodies in the green band is stronger than that in the blue band. Taking advantage of these physical properties and combining them with the bluish and dark appearance of shadows, we propose a simple but effective shadow detection method for multispectral remote sensing images. These physical properties are used to create transformation models that suppress features such as vegetation, water bodies, etc., but at the same time enhance shadows. Then, we transform the shadow representation into a color space to generate candidate shadows using dominant color components. To separate shadows from the others, we propose two indexes, the normalized Color Difference Composite Index (CDCI) and Color Purity Index (CPI), and fuse them to achieve shadows and their confidence. The experimental results indicate that the proposed method can effectively detect the shadows in multispectral images and outperforms the state-of-the-art approaches.

RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery

In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking potential with a focus on image captioning and visual question answering (VQA). In particular, we introduce an improved version of the Large Language and Vision Assistant Model (LLaVA), specifically adapted for RS imagery through a low-rank adaptation approach. To evaluate the model performance, we create the RS-instructions dataset, a comprehensive benchmark dataset that integrates four diverse single-task datasets related to captioning and VQA. The experimental results confirm the model’s effectiveness, marking a step forward toward the development of efficient multi-task models for RS image analysis.

RFL-CDNet: Towards accurate change detection via richer feature learning

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Hyperspectral Image Classification with the Orthogonal Self-Attention ResNet and Two-Step Support Vector Machine

Hyperspectral image classification plays a crucial role in remote sensing image analysis by classifying pixels. However, the existing methods require more spatial–global information interaction and feature extraction capabilities. To overcome these challenges, this paper proposes a novel model for hyperspectral image classification using an orthogonal self-attention ResNet and a two-step support vector machine (OSANet-TSSVM). The OSANet-TSSVM model comprises two essential components: a deep feature extraction network and an improved support vector machine (SVM) classification module. The deep feature extraction network incorporates an orthogonal self-attention module (OSM) and a channel attention module (CAM) to enhance the spatial–spectral feature extraction. The OSM focuses on computing 2D self-attention weights for the orthogonal dimensions of an image, resulting in a reduced number of parameters while capturing comprehensive global contextual information. In contrast, the CAM independently learns attention weights along the channel dimension. The CAM autonomously learns attention weights along the channel dimension, enabling the deep network to emphasise crucial channel information and enhance the spectral feature extraction capability. In addition to the feature extraction network, the OSANet-TSSVM model leverages an improved SVM classification module known as the two-step support vector machine (TSSVM) model. This module preserves the discriminative outcomes of the first-level SVM subclassifier and remaps them as new features for the TSSVM training. By integrating the results of the two classifiers, the deficiencies of the individual classifiers were effectively compensated, resulting in significantly enhanced classification accuracy. The performance of the proposed OSANet-TSSVM model was thoroughly evaluated using public datasets. The experimental results demonstrated that the model performed well in both subjective and objective evaluation metrics. The superiority of this model highlights its potential for advancing hyperspectral image classification in remote sensing applications.