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Keywords = bi-temporal remote sensing images

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28 pages, 8538 KiB  
Article
Deep-Learning Integration of CNN–Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
by Abbas Mohammed Noori, Abdul Razzak T. Ziboon and Amjed N. AL-Hameedawi
Appl. Sci. 2025, 15(14), 7770; https://doi.org/10.3390/app15147770 - 10 Jul 2025
Viewed by 294
Abstract
Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning [...] Read more.
Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article
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18 pages, 2148 KiB  
Article
A Cross-Spatial Differential Localization Network for Remote Sensing Change Captioning
by Ruijie Wu, Hao Ye, Xiangying Liu, Zhenzhen Li, Chenhao Sun and Jiajia Wu
Remote Sens. 2025, 17(13), 2285; https://doi.org/10.3390/rs17132285 - 3 Jul 2025
Viewed by 258
Abstract
Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature [...] Read more.
Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature discrimination. Moreover, direct difference computation after feature extraction tends to retain task-irrelevant noise, limiting the model’s ability to capture meaningful changes. This study proposes a novel cross-spatial Transformer and symmetric difference localization network (CTSD-Net) for RSICC to address these limitations. The proposed Cross-Spatial Transformer adaptively enhances spatial-aware feature representations by guiding the model to focus on key regions across temporal images. Additionally, a hierarchical difference feature integration strategy is introduced to suppress noise by fusing multi-level differential features, while residual-connected high-level features serve as query vectors to facilitate bidirectional change representation learning. Finally, a causal Transformer decoder creates accurate descriptions by linking visual information with text. CTSD-Net achieved BLEU-4 scores of 66.32 and 73.84 on the LEVIR-CC and WHU-CDC datasets, respectively, outperforming existing methods in accurately locating change areas and describing them semantically. This study provides a promising solution for enhancing interpretability in remote sensing change analysis. Full article
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19 pages, 6004 KiB  
Article
Remote Sensing Image Change Detection Based on Dynamic Adaptive Context Attention
by Yong Xie, Yixuan Wang, Xin Wang, Yin Tan and Qin Qin
Symmetry 2025, 17(5), 793; https://doi.org/10.3390/sym17050793 - 20 May 2025
Viewed by 465
Abstract
Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background [...] Read more.
Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder–decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance. Full article
(This article belongs to the Section Computer)
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25 pages, 3960 KiB  
Article
A Semantic-Guided Cross-Attention Network for Change Detection in High-Resolution Remote Sensing Images
by Guowei Lu, Shunyu Yao, Yao Li, Jinbo Tang, Guangyuan Kan, Tao Sun, Changjun Liu, Deqiang Cheng and Ruilong Wei
Remote Sens. 2025, 17(10), 1749; https://doi.org/10.3390/rs17101749 - 16 May 2025
Viewed by 635
Abstract
Remote sensing change detection (CD) involves identifying differences between two satellite images of the same geographic area taken at different times. It plays a critical role in applications such as urban planning and disaster management. Traditional CD methods rely on manually extracted features, [...] Read more.
Remote sensing change detection (CD) involves identifying differences between two satellite images of the same geographic area taken at different times. It plays a critical role in applications such as urban planning and disaster management. Traditional CD methods rely on manually extracted features, which often lack robustness and accuracy in capturing the details of objects. Recently, deep learning-based methods have expanded the applications of CD in high-resolution remote sensing images, yet they struggle to fully utilize the multi-level features extracted by backbone networks, limiting their performance. To address this challenge, we propose a Semantic-Guided Cross-Attention Network (SCANet). It introduces a Hierarchical Semantic-Guided Fusion (HSF) module, which leverages high-level semantic information to guide low-level spatial details through an attention mechanism. Additionally, we design a Cross-Attention Feature Fusion (CAFF) module to establish global correlations between bitemporal images, thereby improving feature interaction. Extensive experiments on the IWHR-data and LEVIR-CD datasets demonstrate that SCANet significantly outperforms existing State-of-the-Art (SOTA) methods. Specifically, the F1-score and the Intersection over Union (IoU) score are improved by 2.002% and 3.297% on the IWHR-data dataset and by 0.761% and 1.276% on the LEVIR-CD dataset, respectively. These results validate the effectiveness of semantic-guided fusion and cross-attention feature interaction, providing new insights for advancing change detection research in high-resolution remote sensing imagery. Full article
(This article belongs to the Section AI Remote Sensing)
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18 pages, 22741 KiB  
Article
Semantic-Aware Remote Sensing Change Detection with Multi-Scale Cross-Attention
by Xingjian Zheng, Xin Lin, Linbo Qing and Xianfeng Ou
Sensors 2025, 25(9), 2813; https://doi.org/10.3390/s25092813 - 29 Apr 2025
Viewed by 622
Abstract
Remote sensing image change detection plays a vital role in diverse real-world applications such as urban development monitoring, disaster assessment, and land use analysis. As deep learning strives, Convolutional Neural Networks (CNNs) have shown their effects in image processing applications. There are two [...] Read more.
Remote sensing image change detection plays a vital role in diverse real-world applications such as urban development monitoring, disaster assessment, and land use analysis. As deep learning strives, Convolutional Neural Networks (CNNs) have shown their effects in image processing applications. There are two problems in old-school change detection techniques: First, the techniques do not fully use the effective information of the global and local features, which causes their semantic comprehension to be less accurate. Second, old-school methods usually simply rely on differences and computation at the pixel level without giving enough attention to the information at the semantic level. To address these problems, we propose a multi-scale cross-attention network (MSCANet) based on a CNN in this paper. First, a multi-scale feature extraction strategy is employed to capture and fuse image information across different spatial resolutions. Second, a cross-attention module is introduced to enhance the model’s ability to comprehend semantic-level changes between bitemporal images. Compared to the existing methods, our approach better integrates spatial and semantic features across scales, leading to more accurate and coherent change detection. Experiments on three public datasets (LEVIR-CD, CDD, and SYSU-CD) demonstrate competitive performance. For example, the model achieves an F1-score of 96.19% and an IoU of 92.67% on the CDD dataset. Additionally, robustness tests with Gaussian noise show that the model maintains high accuracy under input degradation, highlighting its potential for real-world applications. These findings suggest that our MSCANet effectively improves semantic awareness and robustness, offering a promising solution for change detection in complex and noisy remote sensing environments. Full article
(This article belongs to the Section Environmental Sensing)
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25 pages, 17354 KiB  
Article
Frequency–Spatial–Temporal Domain Fusion Network for Remote Sensing Image Change Captioning
by Shiwei Zou, Yingmei Wei, Yuxiang Xie and Xidao Luan
Remote Sens. 2025, 17(8), 1463; https://doi.org/10.3390/rs17081463 - 19 Apr 2025
Viewed by 689
Abstract
Remote Sensing Image Change Captioning (RSICC) has emerged as a cross-disciplinary technology that automatically generates sentences describing the changes in bi-temporal remote sensing images. While demonstrating significant potential for urban planning, agricultural surveillance, and disaster management, current RSICC methods exhibit two fundamental limitations: [...] Read more.
Remote Sensing Image Change Captioning (RSICC) has emerged as a cross-disciplinary technology that automatically generates sentences describing the changes in bi-temporal remote sensing images. While demonstrating significant potential for urban planning, agricultural surveillance, and disaster management, current RSICC methods exhibit two fundamental limitations: (1) vulnerability to pseudo-changes induced by illumination fluctuations and seasonal transitions and (2) an overemphasis on spatial variations with insufficient modeling of temporal dependencies in multi-temporal contexts. To address these challenges, we present the Frequency–Spatial–Temporal Fusion Network (FST-Net), a novel framework that integrates frequency, spatial, and temporal information for RSICC. Specifically, our Frequency–Spatial Fusion module implements adaptive spectral decomposition to disentangle structural changes from high-frequency noise artifacts, effectively suppressing environmental interference. The Spatia–Temporal Modeling module is further developed to employ state-space guided sequential scanning to capture evolutionary patterns of geospatial changes across temporal dimensions. Additionally, a unified dual-task decoder architecture bridges pixel-level change detection with semantic-level change captioning, achieving joint optimization of localization precision and description accuracy. Experiments on the LEVIR-MCI dataset demonstrate that our FSTNet outperforms previous methods by 3.65% on BLEU-4 and 4.08% on CIDEr-D, establishing new performance standards for RSICC. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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26 pages, 15436 KiB  
Article
AGCD: An Attention-Guided Graph Convolution Network for Change Detection of Remote Sensing Images
by Heng Li, Xin Lyu, Xin Li, Yiwei Fang, Zhennan Xu, Xinyuan Wang, Chengming Zhang, Chun Xu, Shaochuan Chen and Chengxin Lu
Remote Sens. 2025, 17(8), 1367; https://doi.org/10.3390/rs17081367 - 11 Apr 2025
Viewed by 542
Abstract
Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, [...] Read more.
Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, leading to high uncertainty in distinguishing true changes from pseudo changes. To overcome these limitations, we propose the Attention-guided Graph convolution network for Change Detection (AGCD), a novel framework that integrates a graph convolutional network (GCN) and an attention mechanism to enhance change-detection performance. AGCD introduces three novel modules, including Graph-level Feature Difference Module (GFDM) for enhanced feature interaction, Multi-scale Feature Fusion Module (MFFM) for detailed semantic representation and Spatial-Temporal Attention Module (STAM) for refined spatial-temporal dependency modeling. These modules enable AGCD to reduce pseudo changes triggered by seasonal variations and varying imaging conditions, thereby improving the accuracy and reliability of change-detection results. Extensive experiments on three benchmark datasets demonstrate that AGCD’s superior performance, achieving the best F1-score of 90.34% and IoU of 82.38% on the LEVIR-CD dataset and outperforming existing state-of-the-art methods by a notable margin. Full article
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18 pages, 2882 KiB  
Article
CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images
by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li and Xinyu Zhao
Remote Sens. 2025, 17(7), 1144; https://doi.org/10.3390/rs17071144 - 24 Mar 2025
Viewed by 1102
Abstract
With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can [...] Read more.
With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. Additionally, these methods fail to capture essential topological and structural information from remote sensing images, resulting in a high false positive rate. To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. Subsequently, a diffusion model is employed to balance the states of nodes within the graph, enabling the co-evolution of adjacency relationships and feature information, thereby aggregating higher-order feature information to obtain superior feature embeddings. The network is trained with a carefully crafted contrastive loss function to effectively capture high-level structural information. Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. The effectiveness and feasibility of the suggested method are confirmed by experimental results on three different datasets, which show that it performs better than several of the top SSL-CD methods. Full article
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21 pages, 8302 KiB  
Article
Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
by Gang Wei, Yuqi Miao and Zhicheng Wang
Appl. Sci. 2025, 15(7), 3475; https://doi.org/10.3390/app15073475 - 21 Mar 2025
Viewed by 859
Abstract
Change detection in remote sensing images is a critical task that requires effectively capturing both global and differential information between bitemporal or more images. Recent progress in foundational vision models, like the Segment Anything Model (SAM), has led to significant improvements in feature [...] Read more.
Change detection in remote sensing images is a critical task that requires effectively capturing both global and differential information between bitemporal or more images. Recent progress in foundational vision models, like the Segment Anything Model (SAM), has led to significant improvements in feature extraction. However, these models do not have specific mechanisms designed to effectively utilize global and differential information for change detection tasks. To address this limitation, we propose Siamese-SAM, a novel Siamese network incorporating SAM as the encoder for each input image. To enhance feature representations, we introduce three specialized modules: the Global Information Enhancement Module (GIEM) to refine global representations, the Differential Information Enhancement Module (DIEM) to emphasize differential features, and the Differential Global Information Fusion Module (DGIF) to integrate global and differential information effectively. Our model is evaluated on three benchmark datasets: LEVIR-CD, SYSU-CD, and GZ-CD, achieving state-of-the-art performance. Specifically, Siamese-SAM attains F1 scores of 92.67%, 82.61%, and 88.79% and IoU scores of 86.34%, 70.17%, and 79.83%, respectively, outperforming conventional approaches. Full article
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing—2nd Edition)
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27 pages, 42566 KiB  
Article
Unsupervised Rural Flood Mapping from Bi-Temporal Sentinel-1 Images Using an Improved Wavelet-Fusion Flood-Change Index (IWFCI) and an Uncertainty-Sensitive Markov Random Field (USMRF) Model
by Amin Mohsenifar, Ali Mohammadzadeh and Sadegh Jamali
Remote Sens. 2025, 17(6), 1024; https://doi.org/10.3390/rs17061024 - 14 Mar 2025
Cited by 2 | Viewed by 801
Abstract
Synthetic aperture radar (SAR) remote sensing (RS) technology is an ideal tool to map flooded areas on account of its all-time, all-weather imaging capability. Existing SAR data-based change detection approaches lack well-discriminant change indices for reliable floodwater mapping. To resolve this issue, an [...] Read more.
Synthetic aperture radar (SAR) remote sensing (RS) technology is an ideal tool to map flooded areas on account of its all-time, all-weather imaging capability. Existing SAR data-based change detection approaches lack well-discriminant change indices for reliable floodwater mapping. To resolve this issue, an unsupervised change detection approach, made up of two main steps, is proposed for detecting floodwaters from bi-temporal SAR data. In the first step, an improved wavelet-fusion flood-change index (IWFCI) is proposed. The IWFCI modifies the mean-ratio change index (CI) to fuse it with the log-ratio CI using the discrete wavelet transform (DWT). The IWFCI also employs a discriminant feature derived from the co-flood image to enhance the separability between the non-flood and flood areas. In the second step, an uncertainty-sensitive Markov random field (USMRF) model is proposed to diminish the over-smoothness issue in the areas with high uncertainty based on a new Gaussian uncertainty term. To appraise the efficacy of the floodwater detection approach proposed in this study, comparative experiments were conducted in two stages on four datasets, each including a normalized difference water index (NDWI) and pre-and co-flood Sentinel-1 data. In the first stage, the proposed IWFCI was compared to a number of state-of-the-art (SOTA) CIs, and the second stage compared USMRF to the SOTA change detection algorithms. From the experimental results in the first stage, the proposed IWFCI, yielding an average F-score of 86.20%, performed better than SOTA CIs. Likewise, according to the experimental results obtained in the second stage, the USMRF model with an average F-score of 89.27% outperformed the comparative methods in classifying non-flood and flood classes. Accordingly, the proposed floodwater detection approach, combining IWFCI and USMRF, can serve as a reliable tool for detecting flooded areas in SAR data. Full article
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27 pages, 8176 KiB  
Article
FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images
by Bochao Chen, Yapeng Wang, Xu Yang, Xiaochen Yuan and Sio Kei Im
Remote Sens. 2025, 17(5), 824; https://doi.org/10.3390/rs17050824 - 26 Feb 2025
Viewed by 955
Abstract
Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection [...] Read more.
Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection methods often struggle with balancing the extraction of fine-grained spatial details and effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. The model also includes a Contextual Dual-Land-Cover Attention Fusion Module (CD-LKAFM) to integrate multi-scale information during the feature recovery stage, improving the resolution of details and the integration of semantic information. Experimental results showed that FFLKCDNet outperformed existing methods on datasets such as GVLM, SYSU, and LEVIR, achieving superior performance in metrics such as Kappa coefficient, mIoU, MPA, and F1 score. The model achieves high-precision change detection for remote sensing images through multi-scale feature fusion, noise suppression, and fine-grained information capture. These advancements pave the way for more precise and reliable applications in urban planning, environmental monitoring, and disaster management. Full article
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22 pages, 17732 KiB  
Article
Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning
by Jinjiang Wei, Kaimin Sun, Wenzhuo Li, Wangbin Li, Song Gao, Shunxia Miao, Yingjiao Tan, Wei Cui and Yu Duan
Remote Sens. 2025, 17(5), 798; https://doi.org/10.3390/rs17050798 - 25 Feb 2025
Viewed by 841
Abstract
Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual style interferences stemming from varying acquisition times, such as radiation, weather, [...] Read more.
Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual style interferences stemming from varying acquisition times, such as radiation, weather, and phenology changes, often lead to false detections. Existing methods struggle to robustly measure background similarity in the presence of such discrepancies and lack quantitative validation for assessing their effectiveness. To address these limitations, we propose Representation Consistency Change Detection (RCCD), a novel deep learning framework that enforces global style and local spatial consistency of features across encoding and decoding stages for robust cross-visual style change detection. RCCD leverages large-kernel convolutional supervision for local spatial context awareness and global content-aware style transfer for feature harmonization, effectively suppressing interference from background variations. Extensive evaluations on S2Looking and LEVIR-CD+ datasets demonstrate RCCD’s superior performance, achieving state-of-the-art F1-scores. Furthermore, on dedicated subsets with large visual style differences, RCCD exhibits more substantial improvements, highlighting its effectiveness in mitigating interference caused by visual style errors. The code has been open-sourced on GitHub. Full article
(This article belongs to the Special Issue Advances in Deep Learning Approaches in Remote Sensing)
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26 pages, 3008 KiB  
Article
HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection
by Lu Zhao, Ling Wan, Lei Ma and Yiming Zhang
Remote Sens. 2025, 17(5), 792; https://doi.org/10.3390/rs17050792 - 24 Feb 2025
Viewed by 598
Abstract
Time series remote sensing images (TSIs) offer essential data for time series remote sensing image change detection with remote sensing technology advances. However, most existing methods focus on bi-temporal images, lacking the exploration of temporal information between images. This presents a significant challenge [...] Read more.
Time series remote sensing images (TSIs) offer essential data for time series remote sensing image change detection with remote sensing technology advances. However, most existing methods focus on bi-temporal images, lacking the exploration of temporal information between images. This presents a significant challenge in effectively utilizing the rich spatio-temporal and object information inherent to TSIs. In this work, we propose a History-Integrated Spatial–Temporal Information Extraction Network (HiSTENet), which comprehensively utilize the spatio-temporal information of TSIs to achieve change detection of continuous image pairs. A Spatial-Temporal Relationship Extraction Module is utilized to model the spatio-temporal relationship. Simultaneously, a Historical Integration Module is introduced to fuse the objects’ characteristics across historical temporal images, while leveraging the features of historical images. Furthermore, the Feature Alignment Fusion Module mitigates pseudo changes by computing feature offsets and aligning images in the feature space. Experiments on SpaceNet7 and DynamicEarthNet demonstrate that HiSTENet outperforms other representative methods, achieving a better balance between precision and recall. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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20 pages, 15946 KiB  
Article
DVF-NET: Bi-Temporal Remote Sensing Image Registration Network Based on Displacement Vector Field Fusion
by Mingliang Xue, Yiming Zhang, Shucai Jia, Chong Cao, Lin Feng and Wanquan Liu
Sensors 2025, 25(5), 1380; https://doi.org/10.3390/s25051380 - 24 Feb 2025
Viewed by 661
Abstract
Accurate image registration is essential for various remote sensing applications, particularly in multi-temporal image analysis. This paper introduces DVF-NET, a novel deep learning-based framework for dual-temporal remote sensing image registration. DVF-NET integrates two displacement vector fields to address nonlinear distortions caused by significant [...] Read more.
Accurate image registration is essential for various remote sensing applications, particularly in multi-temporal image analysis. This paper introduces DVF-NET, a novel deep learning-based framework for dual-temporal remote sensing image registration. DVF-NET integrates two displacement vector fields to address nonlinear distortions caused by significant variations between images, enabling more precise image alignment. A key innovation of this method is the incorporation of a Structural Attention Module (SAT), which enhances the model’s ability to focus on structural features, improving the feature extraction process. Additionally, we propose a novel loss function design that combines multiple similarity metrics, ensuring more comprehensive supervision during training. Experimental results on various remote sensing datasets indicate that the proposed DVF-NET outperforms the existing methods in both accuracy and robustness, particularly when handling images with substantial geometric distortions such as tilted buildings. The results validate the effectiveness of our approach and highlight its potential for various remote sensing tasks, including change detection, land cover classification, and environmental monitoring. DVF-NET provides a promising direction for the advancement of remote sensing image registration techniques, offering both high precision and robustness in complex real-world scenarios. Full article
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17 pages, 4266 KiB  
Article
Hyperspectral Image Change Detection Method Based on the Balanced Metric
by Xintao Liang, Xinling Li, Qingyan Wang, Jiadong Qian and Yujing Wang
Sensors 2025, 25(4), 1158; https://doi.org/10.3390/s25041158 - 13 Feb 2025
Viewed by 689
Abstract
Change detection, as a popular research direction for dynamic monitoring of land cover change, usually uses hyperspectral remote-sensing images as data sources. Hyperspectral images have rich spatial–spectral information, but traditional change detection methods have limited ability to express the features of hyperspectral images, [...] Read more.
Change detection, as a popular research direction for dynamic monitoring of land cover change, usually uses hyperspectral remote-sensing images as data sources. Hyperspectral images have rich spatial–spectral information, but traditional change detection methods have limited ability to express the features of hyperspectral images, and it is difficult to identify the complex detailed features, semantic features, and spatial–temporal correlation features in two-phase hyperspectral images. Effectively using the abundant spatial and spectral information in hyperspectral images to complete change detection is a challenging task. This paper proposes a hyperspectral image change detection method based on the balanced metric, which uses the spatiotemporal attention module to translate bi-temporal hyperspectral images to the same eigenspace, uses the deep Siamese network structure to extract deep semantic features and shallow spatial features, and measures sample features according to the Euclidean distance. In the training phase, the model is optimized by minimizing the loss of distance maps and label maps. In the testing phase, the prediction map is generated by simple thresholding of distance maps. Experiments show that on the four datasets, the proposed method can achieve a good change detection effect. Full article
(This article belongs to the Section Sensing and Imaging)
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