Review of bitemporal change detection using SAR imagery

LIU Yuting; LI Shihua; HE Ze; LIU Kaitong

doi:10.11834/jrs.20255072

Review | Views : 0 下载量: 907 CSCD: 0

R-PDF
PDF
Export
Share
Collection
Album

Review of bitemporal change detection using SAR imagery
Vol. 30, Issue 1, Pages: 11-41(2026)
Received：12 March 2025，

Published：07 January 2026
DOI： 10.11834/jrs.20255072
稿件说明：

移动端阅览

刘玉婷，李世华，何泽，刘开通.2026.基于SAR图像的双时相变化检测研究综述.遥感学报，30（1）： 11-41 DOI： 10.11834/jrs.20255072.

Liu Y T，Li S H，He Z and Liu K T. 2026. Review of bitemporal change detection using SAR imagery. National Remote Sensing Bulletin， 30（1）：11-41 DOI： 10.11834/jrs.20255072.

摘要

遥感变化检测作为一种重要的监测技术，广泛应用于城市规划、灾害评估、资源管理等领域。合成孔径雷达SAR（Synthetic Aperture Radar）具备全天时、全天候成像能力，克服了光学变化检测受天气和光照影响的问题。其微波穿透能力、极化特性和相干成像机制，使其在监测次地表与遮挡区域变化、地物结构与物理特性变化，以及微小变化等方面具有独特优势。目前，大多数遥感双时相变化检测综述主要围绕光学遥感图像展开，缺乏对基于SAR图像变化检测的系统且针对性的总结。此外，随着深度学习与多源融合技术的发展，基于SAR图像的同构和异构变化检测已成为前沿热点。因此，文章基于上百篇国内外经典和最新文献，结合SAR卫星成像原理，首先探讨了不同成像条件下的变化检测典型应用。然后，构建了包括数据获取、图像预处理、变化识别与后处理等环节的双时相变化检测一般流程。在此基础上，系统梳理了主流的变化检测数据和方法。数据部分涵盖基于SAR的同构与异构变化检测开源数据集，方法部分涵盖传统方法以及深度学习方法中的同构与异构变化检测研究。特别地，整理了相关数据集和模型代码链接，为后续领域研究提供重要参考。最后，本文从数据、算法和应用3个层面总结了当前领域面临的主要挑战，如数据质量与数量、模型特征表达能力与计算复杂度、变化检测应用范围等。针对这些挑战，展望了未来多模态融合、智能化与轻量化模型设计以及多元变化检测的发展方向。

Abstract

Remote sensing Change Detection (CD)

as a crucial monitoring technique

has been widely applied in fields such as urban planning

disaster assessment

and resource management. Synthetic aperture radar (SAR) possesses all-weather

all-time imaging capabilities

effectively overcoming the limitations of optical CD that are influenced by weather conditions and illumination variations. Its microwave penetration ability

polarization characteristics

and coherent imaging mechanism provide unique advantages in monitoring subsurface and occluded changes

object structure and physical property variations

and subtle changes. Currently

most existing reviews on bi-temporal CD focus on optical imagery and deep learning algorithms

lacking a systematic and targeted summary of SAR-based CD. Furthermore

with the advancement of deep learning and multisource data fusion

homogeneous and heterogeneous CD based on SAR imagery have emerged as prominent research frontiers.

On the basis of the challenges outlined above

this article draws upon classical and recent literature to first explore the diverse application scenarios and domains of CD under varying imaging conditions. SAR imaging modes mainly include Stripmap

ScanSAR

and Spotlight

each of which has distinct advantages in resolution and coverage

catering to diverse CD tasks. Polarization modes

such as single

dual

and full polarization

affect the change type and richness of information extracted from SAR data. Different frequency bands

such as L-band

C-band

and X-band

exhibit varying penetration capabilities and sensitivities. With the increasing availability of multitemporal SAR and multisource data

the applications of SAR-based CD are expanding. Multitemporal SAR leverages time-series information to enhance the detection of periodic changes

while the fusion of SAR and optical data overcomes single-sensor limitations

thereby advancing multimodal CD methods.

Then

a comprehensive SAR CD framework is constructed

which includes data acquisition

image preprocessing

change identification

and post-processing. During data acquisition

factors such as satellite coverage

imaging mode

and band selection must be considered. Image preprocessing primarily involves image co-registration

radiometric correction

geometric correction

and noise reduction. In the change identification phase

traditional homogeneous CD methods primarily depend on the generation of difference images followed by detailed difference analysis. Conventional heterogeneous CD approaches typically employ post-classification

similarity measurement

and feature space transformation to establish cross-modal correspondences. By contrast

deep learning-based methods utilize neural networks to automatically extract homogeneous or heterogeneous features and identify changes. Finally

the post-processing focuses on accuracy assessment and validation of the detection results.

Moreover

the article systematically reviews the mainstream datasets and methods for SAR-based homogeneous and heterogeneous CD. Homogeneous datasets consist of bi-temporal SAR images from the same sensor

while heterogeneous datasets typically include SAR and optical image pairs. These datasets offer preprocessed imagery and accurate binary change labels to support learning and evaluation. The reviewed methods encompass both traditional and deep learning-based approaches tailored for these two scenarios. Traditional methods offer computational efficiency but rely heavily on manually designed features. Deep learning methods

with their powerful learning capabilities

modality adaptability

and end-to-end modeling advantages

can effectively extract both unimodal and multimodal features. Additionally

curated links to datasets and model codes are provided to support future research in this field.

Finally

the main challenges in the field are summarized from the perspectives of data

algorithms

and applications

including issues such as data quality and availability

model representational capacity and computational complexity

and the limited scope of current CD applications. In response to these challenges

future research directions are proposed

focusing on multimodal data fusion

intelligent and lightweight model design

and diversified CD applications.

关键词

Keywords

references

Alberga V . 2009 . Similarity measures of remotely sensed multi-sensor images for change detection applications . Remote Sensing , 1 ( 3 ): 122 - 143 [ DOI: 10.3390/rs1030122 http://dx.doi.org/10.3390/rs1030122 ]

Alves D I , Palm B G , Hellsten H , Vu V T , Pettersson M I , Machado R , Uchôa-Filho B F and Dammert P . 2020 . Wavelength-resolution SAR change detection using Bayes’ theorem . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 5560 - 5568 [ DOI: 10.1109/JSTARS.2020.3025089 http://dx.doi.org/10.1109/JSTARS.2020.3025089 ]

Ayhan B and Kwan C . 2019 . A new approach to change detection using heterogeneous images // Proceedings of the 2019 10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference . New York : IEEE: 192 - 197 [ DOI: 10.1109/UEMCON47517.2019.8993038 http://dx.doi.org/10.1109/UEMCON47517.2019.8993038 ]

Baronti S , Carla R , Sigismondi S and Alparone L . 1994 . Principal component analysis for change detection on polarimetric multitemporal SAR data // Proceedings of the 1994 IEEE International Geoscience and Remote Sensing Symposium . Pasadena : IEEE: 2152 - 2154 [ DOI: 10.1109/IGARSS.1994.399678 http://dx.doi.org/10.1109/IGARSS.1994.399678 ]

Barreto T L M , Rosa R A S , Wimmer C , Nogueira J B , Almeida J and Cappabianco F A M . 2016 . Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification // Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium . Beijing : IEEE: 5201 - 5204 [ DOI: 10.1109/IGARSS.2016.7730355 http://dx.doi.org/10.1109/IGARSS.2016.7730355 ]

Bazi Y , Bruzzone L and Melgani F . 2005 . An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images . IEEE Transactions on Geoscience and Remote Sensing , 43 ( 4 ): 874 - 887 [ DOI: 10.1109/TGRS.2004.842441 http://dx.doi.org/10.1109/TGRS.2004.842441 ]

Bazi Y , Bruzzone L and Melgani F . 2006 . Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images . IEEE Geoscience and Remote Sensing Letters , 3 ( 3 ): 349 - 353 [ DOI: 10.1109/LGRS.2006.869973 http://dx.doi.org/10.1109/LGRS.2006.869973 ]

Bazi Y , Bruzzone L and Melgani F . 2007 . Image thresholding based on the EM algorithm and the generalized Gaussian distribution . Pattern Recognition , 40 ( 2 ): 619 - 634 [ DOI: 10.1016/j.patcog.2006.05.006 http://dx.doi.org/10.1016/j.patcog.2006.05.006 ]

Bazi Y , Melgani F and Al-Sharari H D . 2010 . Unsupervised change detection in multispectral remotely sensed imagery with level set methods . IEEE Transactions on Geoscience and Remote Sensing , 48 ( 8 ): 3178 - 3187 [ DOI: 10.1109/TGRS.2010.2045506 http://dx.doi.org/10.1109/TGRS.2010.2045506 ]

Bonafilia D , Tellman B , Anderson T and Issenberg E . 2020 . Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Seattle : IEEE : 835 - 845 [ DOI: 10.1109/CVPRW50498.2020.00113 http://dx.doi.org/10.1109/CVPRW50498.2020.00113 ]

Bovolo F and Bruzzone L . 2005 . A detail-preserving scale-driven approach to change detection in multitemporal SAR images . IEEE Transactions on Geoscience and Remote Sensing , 43 ( 12 ): 2963 - 2972 [ DOI: 10.1109/TGRS.2005.857987 http://dx.doi.org/10.1109/TGRS.2005.857987 ]

Bovolo F and Bruzzone L . 2007 . A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain . IEEE Transactions on Geoscience and Remote Sensing , 45 ( 1 ): 218 - 236 [ DOI: 10.1109/TGRS.2006.885408 http://dx.doi.org/10.1109/TGRS.2006.885408 ]

Bovolo F , Marin C and Bruzzone L . 2013 . A hierarchical approach to change detection in very high resolution SAR images for surveillance applications . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 4 ): 2042 - 2054 [ DOI: 10.1109/TGRS.2012.2223219 http://dx.doi.org/10.1109/TGRS.2012.2223219 ]

Brisco B , Schmitt A , Murnaghan K , Kaya S and Roth A . 2013 . SAR polarimetric change detection for flooded vegetation . International Journal of Digital Earth , 6 ( 2 ): 103 - 114 [ DOI: 10.1080/17538947.2011.608813 http://dx.doi.org/10.1080/17538947.2011.608813 ]

Brunner D , Lemoine G and Bruzzone L . 2010 . Earthquake damage assessment of buildings using VHR optical and SAR imagery . IEEE Transactions on Geoscience and Remote Sensing , 48 ( 5 ): 2403 - 2420 [ DOI: 10.1109/TGRS.2009.2038274 http://dx.doi.org/10.1109/TGRS.2009.2038274 ]

Cable J W , Kovacs J M , Shang J L and Jiao X F . 2014 . Multi-temporal polarimetric RADARSAT-2 for land cover monitoring in Northeastern Ontario, Canada . Remote Sensing , 6 ( 3 ): 2372 - 2392 [ DOI: 10.3390/rs6032372 http://dx.doi.org/10.3390/rs6032372 ]

Cao W X and Martinis S . 2015 . SAR-based change detection using hypothesis testing and Markov random field modelling . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W 3 : 783 - 790 [ DOI: 10.5194/isprsarchives-XL-7-W3-783-2015 http://dx.doi.org/10.5194/isprsarchives-XL-7-W3-783-2015 ]

Cartus O , Siqueira P and Kellndorfer J . 2018 . An error model for mapping forest cover and forest cover change using L-band SAR . IEEE Geoscience and Remote Sensing Letters , 15 ( 1 ): 107 - 111 [ DOI: 10.1109/LGRS.2017.2775659 http://dx.doi.org/10.1109/LGRS.2017.2775659 ]

Chan T F and Vese L A . 2001 . Active contours without edges . IEEE Transactions on Image Processing , 10 ( 2 ): 266 - 277 [ DOI: 10.1109/83.902291 http://dx.doi.org/10.1109/83.902291 ]

Chen G , Zhao Y N and Mouthaan K . 2025 . Joint spatial-frequency scattering network for unsupervised SAR image change detection . IEEE Geoscience and Remote Sensing Letters , 22 : 4004505 [ DOI: 10.1109/LGRS.2025.3534251 http://dx.doi.org/10.1109/LGRS.2025.3534251 ]

Chen H R X , Yokoya N , Wu C and Du B . 2022 . Unsupervised multimodal change detection based on structural relationship graph representation learning . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5635318 [ DOI: 10.1109/TGRS.2022.3229027 http://dx.doi.org/10.1109/TGRS.2022.3229027 ]

Chen Y X and Bruzzone L . 2022 . A self-supervised approach to pixel-level change detection in bi-temporal RS images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 4413911 [ DOI: 10.1109/TGRS.2022.3203897 http://dx.doi.org/10.1109/TGRS.2022.3203897 ]

Cheng G L , Huang Y M , Li X T , Lyu S C , Xu Z Y , Zhao H B , Zhao Q and Xiang S M . 2024 . Change detection methods for remote sensing in the last decade: a comprehensive review . Remote Sensing , 16 ( 13 ): 2355 [ DOI: 10.3390/rs16132355 http://dx.doi.org/10.3390/rs16132355 ]

Cheng Y Q , Li H C , Celik T and Zhang F . 2013 . FRFT-based improved algorithm of unsupervised change detection in SAR images via PCA and K-means clustering // Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium . Melbourne : IEEE: 1952 - 1955 [ DOI: 10.1109/IGARSS.2013.6723189 http://dx.doi.org/10.1109/IGARSS.2013.6723189 ]

Conradsen K , Nielsen A A , Schou J and Skriver H . 2003 . A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data . IEEE Transactions on Geoscience and Remote Sensing , 41 ( 1 ): 4 - 19 [ DOI: 10.1109/TGRS.2002.808066 http://dx.doi.org/10.1109/TGRS.2002.808066 ]

Cui B , Peng Y , Zhang Y H , Yin H J , Fang H , Guo S C and Du P J . 2024 . Enhanced edge information and prototype constrained clustering for SAR change detection . IEEE Transactions on Geoscience and Remote Sensing , 62 : 5206116 [ DOI: 10.1109/TGRS.2024.3367970 http://dx.doi.org/10.1109/TGRS.2024.3367970 ]

Cui B , Zhang Y H , Yan L , Wei J J and Huang Q L . 2019 . A SAR change detection method based on the consistency of single-pixel difference and neighbourhood difference . Remote Sensing Letters , 10 ( 5 ): 488 - 495 [ DOI: 10.1080/2150704X.2018.1562256 http://dx.doi.org/10.1080/2150704X.2018.1562256 ]

Dantas C F , Gaetano R and Ienco D . 2024 . Semi-supervised heterogeneous domain adaptation via disentanglement and pseudo-labelling // Proceedings of the 2024 European Conference on Machine Learning and Knowledge Discovery in Databases: Research Track . Vilnius : Springer: 440 - 456 [ DOI: 10.1007/978-3-031-70352-2_26 http://dx.doi.org/10.1007/978-3-031-70352-2_26 ]

Dong H H , Jiao L C , Ma W P , Liu F , Liu X , Li L L and Yang S Y . 2022 . Deep shearlet network for change detection in SAR images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5241115 [ DOI: 10.1109/TGRS.2022.3228776 http://dx.doi.org/10.1109/TGRS.2022.3228776 ]

Dong H H , Ma W P , Jiao L C , Liu F , Liu X and Zhu H . 2024 . Contrastive learning with context-augmented transformer for change detection in SAR images . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 17 : 17710 – 17724 [ DOI: 10.1109/JSTARS.2024.3464735 http://dx.doi.org/10.1109/JSTARS.2024.3464735 ]

Ebel P , Saha S and Zhu X X . 2021 . Fusing multi-modal data for supervised change detection . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII- B3 - 2021 : 243 - 249 [ DOI: 10.5194/isprs-archives-XLIII-B3-2021-243-2021 http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021 ]

Gao Y H , Gao F , Dong J Y , Du Q and Li H C . 2021 . Synthetic aperture radar image change detection via Siamese adaptive fusion network . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 10748 - 10760 [ DOI: 10.1109/JSTARS.2021.3120381 http://dx.doi.org/10.1109/JSTARS.2021.3120381 ]

Gong M G , Cao Y and Wu Q D . 2012 . A neighborhood-based ratio approach for change detection in SAR images . IEEE Geoscience and Remote Sensing Letters , 9 ( 2 ): 307 - 311 [ DOI: 10.1109/LGRS.2011.2167211 http://dx.doi.org/10.1109/LGRS.2011.2167211 ]

Gong M G , Gao T Q , Zhang M Y , Li W , Wang Z B and Li D Z . 2023 . An M-Nary SAR image change detection based on GAN architecture search . IEEE Transactions on Geoscience and Remote Sensing , 61 : 4503718 [ DOI: 10.1109/TGRS.2023.3293190 http://dx.doi.org/10.1109/TGRS.2023.3293190 ]

Gong M G , Li Y , Jiao L C , Jia M and Su L Z . 2014a . SAR change detection based on intensity and texture changes . ISPRS Journal of Photogrammetry and Remote Sensing , 93 : 123 - 135 [ DOI: 10.1016/j.isprsjprs.2014.04.010 http://dx.doi.org/10.1016/j.isprsjprs.2014.04.010 ]

Gong M G , Su L Z , Jia M and Chen W S . 2014b . Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images . IEEE Transactions on Fuzzy Systems , 22 ( 1 ): 98 - 109 [ DOI: 10.1109/TFUZZ.2013.2249072 http://dx.doi.org/10.1109/TFUZZ.2013.2249072 ]

Gong M G , Zhang P Z , Su L Z and Liu J . 2016 . Coupled dictionary learning for change detection from multisource data . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 12 ): 7077 - 7091 [ DOI: 10.1109/TGRS.2016.2594952 http://dx.doi.org/10.1109/TGRS.2016.2594952 ]

Hafner S , Ban Y F and Nascetti A . 2023 . Semi-supervised urban change detection using multi-modal Sentinel-1 SAR and Sentinel-2 MSI data . Remote Sensing , 15 ( 21 ): 5135 [ DOI: 10.3390/rs15215135 http://dx.doi.org/10.3390/rs15215135 ]

Hafner S , Nascetti A , Azizpour H and Ban Y F . 2022 . Sentinel-1 and Sentinel-2 data fusion for urban change detection using a dual stream U-Net . IEEE Geoscience and Remote Sensing Letters , 19 : 4019805 [ DOI: 10.1109/LGRS.2021.3119856 http://dx.doi.org/10.1109/LGRS.2021.3119856 ]

He X N , Zhang S C , Xue B W , Zhao T and Wu T . 2023 . Cross-modal change detection flood extraction based on convolutional neural network . International Journal of Applied Earth Observation and Geoinformation , 117 : 103197 [ DOI: 10.1016/j.jag.2023.103197 http://dx.doi.org/10.1016/j.jag.2023.103197 ]

Hou X , Bai Y P , Xie Y F , Ge H B , Li Y , Shang C J and Shen Q . 2023 . Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images . Knowledge-Based Systems , 264 : 110281 [ DOI: 10.1016/j.knosys.2023.110281 http://dx.doi.org/10.1016/j.knosys.2023.110281 ]

Inglada J and Mercier G . 2007 . A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis . IEEE Transactions on Geoscience and Remote Sensing , 45 ( 5 ): 1432 - 1445 [ DOI: 10.1109/TGRS.2007.893568 http://dx.doi.org/10.1109/TGRS.2007.893568 ]

Irwin K , Braun A , Fotopoulos G , Roth A and Wessel B . 2018 . Assessing single-polarization and dual-polarization TerraSAR-X data for surface water monitoring . Remote Sensing , 10 ( 6 ): 949 [ DOI: 10.3390/rs10060949 http://dx.doi.org/10.3390/rs10060949 ]

Ji G Y , Dong Y W , Bu Y C , Li Y L , Zhou L J and Liang X D . 2018 . Multi-band SAR coherent change detection method based on coherent representation differences of targets . Journal of Radars , 7 ( 4 ): 455 - 464

冀广宇 , 董勇伟 , 卜运成 , 李焱磊 , 周良将 , 梁兴东 . 2018 . 基于目标相干性表征差异的多波段SAR相干变化检测方法 . 雷达学报 , 7 ( 4 ): 455 - 464 [ DOI: 10.12000/jr18020 http://dx.doi.org/10.12000/jr18020 ]

Jiang F L , Huang B , Wu H S , Feng D , Zhou Y , Zhang M Y , Gong M G , Zhao W and Guan Z Y . 2025 . Change masked modality alignment network for multimodal change detection . IEEE Transactions on Geoscience and Remote Sensing , 63 : 4400316 [ DOI: 10.1109/TGRS.2024.3516001 http://dx.doi.org/10.1109/TGRS.2024.3516001 ]

Jiang H W , Peng M , Zhong Y J , Xie H F , Hao Z M , Lin J M , Ma X L a nd Hu X Y . 2022a . A survey on deep learning-based change detection from high-resolution remote sensing images . Remote Sensing , 14 ( 7 ): 1552 [ DOI: 10.3390/rs14071552 http://dx.doi.org/10.3390/rs14071552 ]

Jiang X , Li G , Liu Y , Zhang X P and He Y . 2020 . Change detection in heterogeneous optical and SAR remote sensing images via deep homogeneous feature fusion . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 1551 - 1566 [ DOI: 10.1109/JSTARS.2020.2983993 http://dx.doi.org/10.1109/JSTARS.2020.2983993 ]

Jiang X , Li G , Zhang X P and He Y . 2022b . A semisupervised siamese network for efficient change detection in heterogeneous remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 4700718 [ DOI: 10.1109/TGRS.2021.3061686 http://dx.doi.org/10.1109/TGRS.2021.3061686 ]

Jing W , Bai H C , Song B B , Ni W P , Wu J Z and Wang Q . 2025 . HeteCD: feature consistency alignment and difference mining for heterogeneous remote sensing image change detection . ISPRS Journal of Photogrammetry and Remote Sensing , 223 : 317 - 327 [ DOI: 10.1016/j.isprsjprs.2025.03.008 http://dx.doi.org/10.1016/j.isprsjprs.2025.03.008 ]

Khoshnevis S A and Ghorshi S . 2020 . A tutorial on tomographic synthetic aperture radar methods . SN Applied Sciences , 2 ( 9 ): 1504 [ DOI: 10.1007/s42452-020-03298-6 http://dx.doi.org/10.1007/s42452-020-03298-6 ]

Kittler J and Illingworth J . 1986 . Minimum error thresholding . Pattern Recognition , 19 ( 1 ): 41 - 47 [ DOI: 10.1016/0031-3203(86)90030-0 http://dx.doi.org/10.1016/0031-3203(86)90030-0 ]

Koeniguer E , Weissgerber F and Janez F . 2014 . Urban change detection by comparing SAR images at different resolutions and polarimetric modes // Proceedings of the 2014 10th European Conference on Synthetic Aperture Radar . Berlin : VDE: 1 - 4

Lei T , Jia X H , Zhang Y N , He L F , Meng H Y and Nandi A K . 2018 . Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering . IEEE Transactions on Fuzzy Systems , 26 ( 5 ): 3027 - 3041 [ DOI: 10.1109/TFUZZ.2018.2796074 http://dx.doi.org/10.1109/TFUZZ.2018.2796074 ]

Li H C , Yang G , Yang W , Du Q and Emery W J . 2020 . Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection . ISPRS Journal of Photogrammetry and Remote Sensing , 160 : 167 - 179 [ DOI: 10.1016/j.isprsjprs.2019.12.002 http://dx.doi.org/10.1016/j.isprsjprs.2019.12.002 ]

Li H L , Zou B , Zhang L M and Qin J . 2024a . CausalCD: a causal graph contrastive learning framework for self-supervised SAR image change detection . IEEE Transactions on Geoscience and Remote Sensing , 62 : 5217016 [ DOI: 10.1109/TGRS.2024.3427848 http://dx.doi.org/10.1109/TGRS.2024.3427848 ]

Li H L , Zou B , Zhang L M and Qin J . 2024b . Semi-supervised SAR image change detection via structure-optimized complex-valued graph contrastive learning . IEEE Geoscience and Remote Sensing Letters , 21 : 4012905 [ DOI: 10.1109/LGRS.2024.3419155 http://dx.doi.org/10.1109/LGRS.2024.3419155 ]

Li J J , Jiao L C , Zhang X R and Yang D D . 2010 . Change detection for SAR images based on joint-classification of bi-temporal images . Journal of Infrared and Millimeter Waves , 28 ( 6 ): 466 - 471 [ DOI: 10.3724/sp.J.1010.2009.00466 http://dx.doi.org/10.3724/sp.J.1010.2009.00466 ]

Liu L Y , Jia Z H , Yang J and Kasabov N K . 2019 . SAR image change detection based on mathematical morphology and the K-means clustering algorithm . IEEE Access , 7 : 43970 - 43978 [ DOI: 10.1109/ACCESS.2019.2908282 http://dx.doi.org/10.1109/ACCESS.2019.2908282 ]

Liu T F , Xu J J , Lei T , Wang Y B , Du X G , Zhang W C , Lv Z Y and Gong M G . 2025 . AEKAN: exploring superpixel-based autoencoder Kolmogorov-Arnold network for unsupervised multimodal change detection . IEEE Transactions on Geoscience and Remote Sensing , 63 : 5601114 [ DOI: 10.1109/TGRS.2024.3515258 http://dx.doi.org/10.1109/TGRS.2024.3515258 ]

Liu Z , Lin Y T , Cao Y , Hu H , Wei Y X , Zhang Z , Lin S and Guo B N . 2021a . Swin Transformer: hierarchical vision transformer using shifted windows // Proceedings of the 2011 IEEE/CVF International Conference on Computer Vision . Montreal : IEEE: 9992 - 10002 [ DOI: 10.1109/ICCV48922.2021.00986 http://dx.doi.org/10.1109/ICCV48922.2021.00986 ]

Liu Z G , Li G , Mercier G , He Y and Pan Q . 2018 . Change detection in heterogenous remote sensing images via homogeneous pixel transformation . IEEE Transactions on Image Processing , 27 ( 4 ): 1822 - 1834 [ DOI: 10.1109/TIP.2017.2784560 http://dx.doi.org/10.1109/TIP.2017.2784560 ]

Liu Z G , Qiu G H , Mercier G and Pan Q . 2021b . A transfer classification method for heterogeneous data based on evidence theory . IEEE Transactions on Systems, Man, and Cybernetics : Systems , 51 ( 8 ): 5129 - 5141 [ DOI: 10.1109/TSMC.2019.2945808 http://dx.doi.org/10.1109/TSMC.2019.2945808 ]

Liu Z G , Zhang L , Li G and He Y . 2017 . Change detection in heterogeneous remote sensing images based on the fusion of pixel transformation // Proceedings of the 2017 20th IEEE International Conference on Information Fusion . Xi'an : IEEE: 1 - 6 [ DOI: 10.23919/ICIF.2017.8009656 http://dx.doi.org/10.23919/ICIF.2017.8009656 ]

Lu T J , Zhong X and Zhong L . 2024 . mSwinUNet: a multi-modal U-shaped swin transformer for supervised change detection . Journal of Intelligent and Fuzzy Systems : Applications in Engineering and Technology , 46 ( 2 ): 4243 - 4252 [ DOI: 10.3233/jifs-233868 http://dx.doi.org/10.3233/jifs-233868 ]

Lu Z and Meyer D J . 2002 . Study of high SAR backscattering caused by an increase of soil moisture over a sparsely vegetated area: implications for characteristics of backscattering . International Journal of Remote Sensing , 23 ( 6 ): 1063 - 1074 [ DOI: 10.1080/01431160110040035 http://dx.doi.org/10.1080/01431160110040035 ]

Luppino L T , Bianchi F M , Moser G and Anfinsen S N . 2019 . Unsupervised image regression for heterogeneous change detection . IEEE Transactions on Geoscience and Remote Sensing , 57 ( 12 ): 9960 - 9975 [ DOI: 10.1109/TGRS.2019.2930348 http://dx.doi.org/10.1109/TGRS.2019.2930348 ]

Luppino L T , Kampffmeyer M , Bianchi F M , Moser G , Serpico S B , Jenssen R and Anfinsen S N . 2022 . Deep image translation with an affinity-based change prior for unsupervised multimodal change detection . IEEE Transactions on Geoscience and Remote Sensing , 60 : 4700422 [ DOI: 10.1109/TGRS.2021.3056196 http://dx.doi.org/10.1109/TGRS.2021.3056196 ]

Marin C , Bovolo F and Bruzzone L . 2015 . Building change detection in multitemporal very high resolution SAR Images . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 5 ): 2664 - 2682 [ DOI: 10.1109/TGRS.2014.2363548 http://dx.doi.org/10.1109/TGRS.2014.2363548 ]

Meng W H , Wang L J , Du A Y and Li Y M . 2020 . SAR image change detection based on data optimization and self-supervised learning . IEEE Access , 8 : 217290 - 217305 [ DOI: 10.1109/ACCESS.2020.3042017 http://dx.doi.org/10.1109/ACCESS.2020.3042017 ]

Mercier G , Moser G and Serpico S B . 2008 . Conditional copulas for change detection in heterogeneous remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 46 ( 5 ): 1428 - 1441 [ DOI: 10.1109/TGRS.2008.916476 http://dx.doi.org/10.1109/TGRS.2008.916476 ]

Moghimi A , Khazai S and Mohammadzadeh A . 2017 . An improved fast level set method initialized with a combination of K-means clustering and Otsu thresholding for unsupervised change detection from SAR images . Arabian Journal of Geosciences , 10 ( 13 ): 293 [ DOI: 10.1007/s12517-017-3072-3 http://dx.doi.org/10.1007/s12517-017-3072-3 ]

Monti-Guarnieri A V , Brovelli M A , Manzoni M , d’Alessandro M M , Molinari M E and Oxoli D . 2018 . Coherent change detection for multipass SAR . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 11 ): 6811 - 6822 [ DOI: 10.1109/TGRS.2018.2843560 http://dx.doi.org/10.1109/TGRS.2018.2843560 ]

Moser G , Serpico S B and Vernazza G . 2013 . Multiresolution SAR data fusion for unsupervised change detection // Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium . Melbourne : IEEE: 1234 - 1237 [ DOI: 10.1109/IGARSS.2013.6723003 http://dx.doi.org/10.1109/IGARSS.2013.6723003 ]

Mubea K and Menz G . 2012 . Monitoring land-use change in Nakuru (Kenya) using multi-sensor satellite data . Advances in Remote Sensing , 1 ( 3 ): 74 - 84 [ DOI: 10.4236/ars.2012.13008 http://dx.doi.org/10.4236/ars.2012.13008 ]

Novak L M . 2005 . Coherent change detection for multi-polarization SAR // Proceedings of the 2005 IEEE Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers . Pacific Grove : IEEE: 568 - 573 [ DOI: 10.1109/ACSSC.2005.1599813 http://dx.doi.org/10.1109/ACSSC.2005.1599813 ]

Otsu N . 1979 . A threshold selection method from gray-level histograms . IEEE Transactions on Systems, Man, and Cybernetics , 9 ( 1 ): 62 - 66 [ DOI: 10.1109/TSMC.1979.4310076 http://dx.doi.org/10.1109/TSMC.1979.4310076 ]

Peng Y , Cui B , Yin H J , Zhang Y H and Du P J . 2022 . Automatic SAR change detection based on visual saliency and multi-hierarchical fuzzy clustering . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 15 : 7755 - 7769 [ DOI: 10.1109/JSTARS.2022.3199017 http://dx.doi.org/10.1109/JSTARS.2022.3199017 ]

Pirrone D , Bovolo F and Bruzzone L . 2016 . A novel framework for change detection in bi-temporal polarimetric SAR images // Proceedings of SPIE 10004, Image and Signal Processing for Remote Sensing XXII . Edinburgh : SPIE: 100040 Z [ DOI: 10.1117/12.2241636 http://dx.doi.org/10.1117/12.2241636 ]

Pun T . 1980 . A new method for grey-level picture thresholding using the entropy of the histogram . Signal Processing , 2 ( 3 ): 223 - 237 [ DOI: 10.1016/0165-1684(80)90020-1 http://dx.doi.org/10.1016/0165-1684(80)90020-1 ]

Qi Z X , Yeh A G O , Li X and Zhang X H . 2015 . A three-component method for timely detection of land cover changes using polarimetric SAR images . ISPRS Journal of Photogrammetry and Remote Sensing , 107 : 3 - 21 [ DOI: 10.1016/j.isprsjprs.2015.02.004 http://dx.doi.org/10.1016/j.isprsjprs.2015.02.004 ]

Qu X F , Gao F , Dong J Y , Du Q and Li H C . 2022 . Change detection in synthetic aperture radar images using a dual-domain network . IEEE Geoscience and Remote Sensing Letters , 19 : 4013405 [ DOI: 10.1109/LGRS.2021.3073900 http://dx.doi.org/10.1109/LGRS.2021.3073900 ]

Ratha D , De S , Celik T and Bhattacharya A . 2017 . Change detection in polarimetric SAR images using a geodesic distance between scattering mechanisms . IEEE Geoscience and Remote Sensing Letters , 14 ( 7 ): 1066 - 1070 [ DOI: 10.1109/LGRS.2017.2696158 http://dx.doi.org/10.1109/LGRS.2017.2696158 ]

Rignot E J M and van Zyl J J . 1993 . Change detection techniques for ERS-1 SAR data . IEEE Transactions on Geoscience and Remote Sensing , 31 ( 4 ): 896 - 906 [ DOI: 10.1109/36.239913 http://dx.doi.org/10.1109/36.239913 ]

Rosa R A S , Fernandes D , Barreto T L M , Wimmer C and Nogueira J B . 2017 . Deforestation detection in Amazon rainforest with multitemporal X-band and P-band SAR images using cross-coherences and superpixels // Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium . Fort Worth : IEEE: 3015 - 3018 [ DOI: 10.1109/IGARSS.2017.8127633 http://dx.doi.org/10.1109/IGARSS.2017.8127633 ]

Saha S , Ebel P and Zhu X X . 2022 . Self-supervised multisensor change detection . IEEE Transactions on Geoscience and Remote Sensing , 60 : 4405710 [ DOI: 10.1109/TGRS.2021.3109957 http://dx.doi.org/10.1109/TGRS.2021.3109957 ]

Saleh T , Holail S , Zahran M , Xiao X W and Xia G S . 2024a . LiST-Net: enhanced flood mapping with lightweight SAR transformer network and dimension-wise attention . IEEE Transactions on Geoscience and Remote Sensing , 62 : 5211817 [ DOI: 10.1109/TGRS.2024.3397797 http://dx.doi.org/10.1109/TGRS.2024.3397797 ]

Saleh T , Weng X X , Holail S , Hao C and Xia G S . 2024b . DAM-Net: flood detection from SAR imagery using differential attention metric-based vision transformers . ISPRS Journal of Photogrammetry and Remote Sensing , 212 : 440 - 453 [ DOI: 10.1016/j.isprsjprs.2024.05.018 http://dx.doi.org/10.1016/j.isprsjprs.2024.05.018 ]

Shafique A , Cao G , Khan Z , Asad M and Aslam M . 2022 . Deep learning-based change detection in remote sensing images: a review . Remote Sensing , 14 ( 4 ): 871 [ DOI: 10.3390/rs14040871 http://dx.doi.org/10.3390/rs14040871 ]

Shen Y , Yao S , Qiang Z K and Pei G X . 2025 . SD-Mamba: a lightweight synthetic-decompression network for cross-modal flood change detection . International Journal of Applied Earth Observation and Geoinformation , 136 : 104409 [ DOI: 10.1016/j.jag.2025.104409 http://dx.doi.org/10.1016/j.jag.2025.104409 ]

Shi W Z , Zhang M , Zhang R , Chen S X and Zhan Z . 2020 . Change detection based on artificial intelligence: state-of-the-art and challenges . Remote Sensing , 12 ( 10 ): 1688 [ DOI: 10.3390/rs12101688 http://dx.doi.org/10.3390/rs12101688 ]

Shi X , Xing M D , Zhang J S , Liu H T and Wang H X . 2024 . SAR multi-satellite collaborative complex area observation planning based on improved genetic algorithm . National Remote Sensing Bulletin , 28 ( 7 ): 1822 - 1834

石鑫 , 邢孟道 , 张金松 , 刘会涛 , 王虹现 . 2024 . 基于改进遗传算法的SAR多星协同复杂区域观测规划 . 遥感学报 , 28 ( 7 ): 1822 - 1834 [ DOI: 10.11834/jrs.20243258 http://dx.doi.org/10.11834/jrs.20243258 ]

Shuai Y M , Xu X , Sun H and Xu G . 2006 . Change detection based on region likelihood ratio in multitemporal SAR images // Proceedings of the 2006 8th International Conference on Signal Processing . Guilin : IEEE: 1 [DOI: 10.1109/ICOSP.2006.345675 ]

Solarna D , Moser G and Serpico S B . 2019 . Multiresolution and multimodality SAR data fusion based on markov and conditional random fields for unsupervised change detection // Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 29 - 32 [ DOI: 10.1109/IGARSS.2019.8898122 http://dx.doi.org/10.1109/IGARSS.2019.8898122 ]

Sumaiya M N and Shantha Selva Kumari R . 2017 . Gabor filter based change detection in SAR images by KI thresholding . Optik , 130 : 114 - 122 [ DOI: 10.1016/j.ijleo.2016.11.040 http://dx.doi.org/10.1016/j.ijleo.2016.11.040 ]

Sun Y L , Lei L , Guan D D , Kuang G Y , Li Z and Liu L . 2025a . Locality preservation for unsupervised multimodal change detection in remote sensing imagery . IEEE Transactions on Neural Networks and Learning Systems , 36 ( 4 ): 6955 - 6969 [ DOI: 10.1109/TNNLS.2024.3401696 http://dx.doi.org/10.1109/TNNLS.2024.3401696 ]

Sun Y L , Lei L , Li X , Sun H and Kuang G Y . 2021 . Nonlocal patch similarity based heterogeneous remote sensing change detection . Pattern Recognition , 109 : 107598 [ DOI: 10.1016/j.patcog.2020.107598 http://dx.doi.org/10.1016/j.patcog.2020.107598 ]

Sun Y L , Lei L , Liu L and Kuang G Y . 2023 . Structural regression fusion for unsupervised multimodal change detection . IEEE Transactions on Geoscience and Remote Sensing , 61 : 4504018 [ DOI: 10.1109/TGRS.2023.3294884 http://dx.doi.org/10.1109/TGRS.2023.3294884 ]

Sun Y L , Li M , Lei L , Li Z and Kuang G Y . 2025b . Signed graph-based image transformation for heterogeneous change detection . IEEE Transactions on Geoscience and Remote Sensing , 63 : 4403618 [ DOI: 10.1109/TGRS.2025.3539732 http://dx.doi.org/10.1109/TGRS.2025.3539732 ]

Tang Y Q , Lin Z F , Han T , Yang X , Zou B and Feng H H . 2024 . Optical and SAR image change detection based on a symmetric network . National Remote Sensing Bulletin , 28 ( 6 ): 1560 - 1575

汤玉奇 , 林泽锋 , 韩特 , 杨欣 , 邹滨 , 冯徽徽 . 2024 . 基于对称网络的光学和SAR影像变化检测 . 遥感学报 , 28 ( 6 ): 1560 - 1575 [ DOI: 10.11834/jrs.20232027 http://dx.doi.org/10.11834/jrs.20232027 ]

Tiampo K F , Huang L C , Simmons C , Woods C and Glasscoe M T . 2022 . Detection of flood extent using Sentinel-1A/B synthetic aperture radar: an application for Hurricane Harvey, Houston, TX . Remote Sensing , 14 ( 9 ): 2261 [ DOI: 10.3390/rs14092261 http://dx.doi.org/10.3390/rs14092261 ]

Tirandaz Z and Akbarizadeh G . 2016 . A two-phase algorithm based on kurtosis curvelet energy and unsupervised spectral regression for segmentation of SAR images . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 ( 3 ): 1244 - 1264 [ DOI: 10.1109/JSTARS.2015.2492552 http://dx.doi.org/10.1109/JSTARS.2015.2492552 ]

Wahl D E , Yocky D A , Jakowatz C V and Simonson K M . 2016 . A new maximum-likelihood change estimator for two-pass SAR coherent change detection . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 4 ): 2460 - 2469 [ DOI: 10.1109/TGRS.2015.2502219 http://dx.doi.org/10.1109/TGRS.2015.2502219 ]

Wan L , Xiang Y M and You H J . 2019a . An object-based hierarchical compound classification method for change detection in heterogeneous optical and SAR images . IEEE Transactions on Geoscience and Remote Sensing , 57 ( 12 ): 9941 - 9959 [ DOI: 10.1109/TGRS.2019.2930322 http://dx.doi.org/10.1109/TGRS.2019.2930322 ]

Wan L , Xiang Y M and You H J . 2019b . A post-classification comparison method for SAR and optical images change detection . IEEE Geoscience and Remote Sensing Letters , 16 ( 7 ): 1026 - 1030 [ DOI: 10.1109/LGRS.2019.2892432 http://dx.doi.org/10.1109/LGRS.2019.2892432 ]

Wan L , Zhang T and You H J . 2018 . Multi-sensor remote sensing image change detection based on sorted histograms . International Journal of Remote Sensing , 39 ( 11 ): 3753 - 3775 [ DOI: 10.1080/01431161.2018.1448481 http://dx.doi.org/10.1080/01431161.2018.1448481 ]

Wang J , Wang Y H , Chen B and Liu H W . 2021 . LCS-EnsemNet: a semisupervised deep neural network for SAR image change detection with dual feature extraction and label-consistent self-ensemble . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 11903 - 11925 [ DOI: 10.1109/JSTARS.2021.3122461 http://dx.doi.org/10.1109/JSTARS.2021.3122461 ]

Wang J , Wang Y H and Liu H W . 2022a . Hybrid variability aware network (HVANet): a self-supervised deep framework for label-free SAR image change detection . Remote Sensing , 14 ( 3 ): 734 [ DOI: 10.3390/rs14030734 http://dx.doi.org/10.3390/rs14030734 ]

Wang J J , Gao F , Dong J Y , Zhang S and Du Q . 2022b . Change detection from synthetic aperture radar images via graph-based knowledge supplement network . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 15 : 1823 - 1836 [ DOI: 10.1109/JSTARS.2022.3146167 http://dx.doi.org/10.1109/JSTARS.2022.3146167 ]

Wang R F , Wang L , Wei X H , Chen J W and Jiao L C . 2022c . Dynamic graph-level neural network for SAR image change detection . IEEE Geoscience and Remote Sensing Letters , 19 : 4501005 [ DOI: 10.1109/LGRS.2021.3132167 http://dx.doi.org/10.1109/LGRS.2021.3132167 ]

Wang S N , Liu Y , Li L L , Zhang Z H and Wang D . 2023 . A summary of SAR image change detection . Laser Journal , 44 ( 1 ): 8 - 15

王少娜 , 刘阳 , 李林林 , 张正华 , 王迪 . 2023 . SAR图像变化检测综述 . 激光杂志 , 44 ( 1 ): 8 - 15 [ DOI: 10.14016/j.cnki.jgzz.2023.01.008 http://dx.doi.org/10.14016/j.cnki.jgzz.2023.01.008 ]

Wang T and Liao M S . 2018 . Coseismic displacement derived from Sentinel-1 data: latest techniques and case studies . Journal of Remote Sensing (in Chinese) , 22 ( S1 ): 120 - 127

王腾 , 廖明生 . 2018 . Sentinel-1卫星数据提取同震形变场: 最新技术及震例 . 遥感学报 , 22 ( S1 ): 120 - 127 [ DOI: 10.11834/jrs.20187480 http://dx.doi.org/10.11834/jrs.20187480 ]

Wang X H , Cheng W , Feng Y N and Song R X . 2023 . TSCNet: topological structure coupling network for change detection of heterogeneous remote sensing images . Remote Sensing , 15 ( 3 ): 621 [ DOI: 10.3390/rs15030621 http://dx.doi.org/10.3390/rs15030621 ]

Watanabe M , Koyama C , Hayashi M , Nagatani I , Tadono T and Shimada M . 2018 . Semi-automatic deforestation detection algorithm with PALSAR-2/ScanSAR HH/HV polarizations // Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia : IEEE: 4177 - 4180 [ DOI: 10.1109/IGARSS.2018.8519006 http://dx.doi.org/10.1109/IGARSS.2018.8519006 ]

Watanabe M , Koyama C N , Hayashi M , Nagatani I , Tadono T and Shimada M . 2021 . Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions . Remote Sensing of Environment , 265 : 112643 [ DOI: 10.1016/j.rse.2021.112643 http://dx.doi.org/10.1016/j.rse.2021.112643 ]

Wu H R , Geng J and Jiang W . 2024a . Multidomain constrained translation network for change detection in heterogeneous remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 62 : 5616916 [ DOI: 10.1109/TGRS.2024.3381196 http://dx.doi.org/10.1109/TGRS.2024.3381196 ]

Wu J S , Yang S W , Li Y K , Fu Y K , Shi Z and Zheng Y . 2024b . Self-supervised change detection of heterogeneous images based on difference algorithms . European Journal of Remote Sensing , 57 ( 1 ): 2372854 [ DOI: 10.1080/22797254.2024.2372854 http://dx.doi.org/10.1080/22797254.2024.2372854 ]

Wu J Z , Li B , Qin Y , Ni W P , Zhang H , Fu R G and Sun Y L . 2021 . A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images . International Journal of Applied Earth Observation and Geoinformation , 105 : 102615 [ DOI: 10.1016/j.jag.2021.102615 http://dx.doi.org/10.1016/j.jag.2021.102615 ]

Xing M D , Lin H , Chen J L , Sun G C and Yan B B . 2019 . A review of imaging algorithms in multi-platform-borne synthetic aperture radar . Journal of Radars , 8 ( 6 ): 732 - 757

邢孟道 , 林浩 , 陈溅来 , 孙光才 , 严棒棒 . 2019 . 多平台合成孔径雷达成像算法综述 . 雷达学报 , 8 ( 6 ): 732 - 757 [ DOI: 10.12000/jr19102 http://dx.doi.org/10.12000/jr19102 ]

Yadav R , Nascetti A and Ban Y F . 2022 . Attentive dual stream siamese U-Net for flood detection on multi-temporal Sentinel-1 data // Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium . Kuala Lumpur : IEEE: 5222 - 5225 [ DOI: 10.1109/IGARSS46834.2022.9883132 http://dx.doi.org/10.1109/IGARSS46834.2022.9883132 ]

Yan T Y , Wan Z F , Zhang P P , Cheng G and Lu H C . 2023 . TransY-Net: learning fully transformer networks for change detection of remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 61 : 4410012 [ DOI: 10.1109/TGRS.2023.3327253 http://dx.doi.org/10.1109/TGRS.2023.3327253 ]

Yang M J , Jiao L C , Liu F , Hou B , Yang S Y , Zhang Y K and Wang J L . 2023a . Multicue contrastive self-supervised learning for change detection in remote sensing . IEEE Transactions on Geoscience and Remote Sensing , 61 : 5221014 [ DOI: 10.1109/TGRS.2023.3330494 http://dx.doi.org/10.1109/TGRS.2023.3330494 ]

Yang Z F , Wu Y , Li M , Hu X and Li Z K . 2023b . Unsupervised change detection in PolSAR images using siamese encoder–decoder framework based on graph-context attention network . International Journal of Applied Earth Observation and Geoinformation , 124 : 103511 [ DOI: 10.1016/j.jag.2023.103511 http://dx.doi.org/10.1016/j.jag.2023.103511 ]

Zhan T , Dang Q L and Zhu Y Y . 2024 . Neighborhood difference-based self-supervised network for detecting small changes from synthetic aperture radar images . IEEE Geoscience and Remote Sensing Letters , 21 : 4012305 [ DOI: 10.1109/LGRS.2024.3415819 http://dx.doi.org/10.1109/LGRS.2024.3415819 ]

Zhang C X , Feng Y K , Hu L , Tapete D , Pan L , Liang Z H , Cigna F and Yue P . 2022 . A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images . International Journal of Applied Earth Observation and Geoinformation , 109 : 102769 [ DOI: 10.1016/j.jag.2022.102769 http://dx.doi.org/10.1016/j.jag.2022.102769 ]

Zhang D J , Hong Y T , Qiu X J , Dong L , Yan D Q and Peng C B . 2024 . C²F²: cross-task cross-domain feature fusion for semi-supervised change detection . IEEE Geoscience and Remote Sensing Letters , 21 : 6016405 [ DOI: 10.1109/LGRS.2024.3467260 http://dx.doi.org/10.1109/LGRS.2024.3467260 ]

Zhang E L , Zong H , Li X Y , Feng M C and Ren J C . 2025 . ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection . IEEE Transactions on Geoscience and Remote Sensing , 63 : 5501516 [ DOI: 10.1109/TGRS.2024.3519195 http://dx.doi.org/10.1109/TGRS.2024.3519195 ]

Zhang H , Qi Z X , Li X , Chen Y M , Wang X W and He Y Q . 2021 . An urban flooding index for unsupervised inundated urban area detection using Sentinel-1 polarimetric SAR images . Remote Sensing , 13 ( 22 ): 4511 [ DOI: 10.3390/rs13224511 http://dx.doi.org/10.3390/rs13224511 ]

Zhang Y , Wang S G , Wang C , Li J X and Zhang H . 2018 . SAR image change detection using saliency extraction and shearlet transform . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 12 ): 4701 - 4710 [ DOI: 10.1109/JSTARS.2018.2866540 http://dx.doi.org/10.1109/JSTARS.2018.2866540 ]

Zhang Y H , Zhang X F , Fu J and Jin S S . 2014 . A SAR change detection method based on polarimetric distance measure . Acta Geodaetica et Cartographica Sinica , 43 ( 2 ): 143 - 150

张永红 , 张现峰 , 付姣 , 金姗姗 . 2014 . 一种基于极化距离测度的SAR变化检测方法 . 测绘学报 , 43 ( 2 ): 143 - 150 [ DOI: 10.13485/j.cnki.11-2089.2014.0021 http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0021 ]

Zheng N R , Yang Z A , Shi X Z , Yang H , Sun Y and Wang F . 2024 . Airborne multidimensional SAR land cover dataset and fusion classification method . National Remote Sensing Bulletin , 28 ( 9 ): 2209 - 2222

郑乃榕 , 杨子安 , 施贤正 , 杨宏 , 孙越 , 王峰 . 2024 . 机载多维度SAR图像地物数据集构建及融合分类方法 . 遥感学报 , 28 ( 9 ): 2209 - 2222 [ DOI: 10.11834/jrs.20242276 http://dx.doi.org/10.11834/jrs.20242276 ]

Zheng Y G , Zhang X R , Hou B and Liu G C . 2014 . Using combined difference image and k -means clustering for SAR image change detection . IEEE Geoscience and Remote Sensing Letters , 11 ( 3 ): 691 - 695 [ DOI: 10.1109/LGRS.2013.2275738 http://dx.doi.org/10.1109/LGRS.2013.2275738 ]

Zhu L Y , Sun W B , Fan D Q , Xing H Q and Liu X Q . 2024a . Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images . Pattern Recognition , 149 : 110237 [ DOI: 10.1016/j.patcog.2023.110237 http://dx.doi.org/10.1016/j.patcog.2023.110237 ]

Zhu Q Q , Guo X , Li Z Q and Li D R . 2024b . A review of multi-class change detection for satellite remote sensing imagery . Geo-spatial Information Science , 27 ( 1 ): 1 - 15 [ DOI: 10.1080/10095020.2022.2128902 http://dx.doi.org/10.1080/10095020.2022.2128902 ]

Zhuang H F , Fan H D , Deng K Z and Yu Y . 2018 . An improved neighborhood-based ratio approach for change detection in SAR images . European Journal of Remote Sensing , 51 ( 1 ): 723 - 738 [ DOI: 10.1080/22797254.2018.1482523 http://dx.doi.org/10.1080/22797254.2018.1482523 ]

Zou C Y , Zhao L L , Sun P L , Fu B X and Su X . 2022 . New building detection using sar images with different resolutions // Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium . Kuala Lumpur : IEEE: 3680 - 3683 [ DOI: 10.1109/IGARSS46834.2022.9884848 http://dx.doi.org/10.1109/IGARSS46834.2022.9884848 ]