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纸质出版日期： 2024-06-16 ，
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丁晨， 陈静怡， 郑萌萌， 张磊， 魏巍， 张艳宁. 2024. 高光谱图像变化检测技术研究进展. 中国图象图形学报， 29(06):1714-1729
Ding Chen， Chen Jingyi， Zheng Mengmeng， Zhang Lei， Wei wei， Zhang Yanning. 2024. Survey of hyperspectral image change detection method. Journal of Image and Graphics， 29(06):1714-1729
相对于自然图像和多光谱图像，高光谱图像包含丰富的空间—光谱信息，不仅能够保留目标的空间信息，还能够获取高度可辨别的光谱信息。因此，如变化检测、目标追踪等高光谱图像处理技术在对地观测任务中得到了广泛应用。然而，在高光谱图像变化检测的过程中仍然存在许多问题与挑战。如高光谱图像的高维复杂性、光谱差异性以及存在光谱混合等问题，影响变化检测效果。得益于深度学习理论的深入研究，高光谱图像变化检测技术研究得到了极大的发展。本文对现有基于深度学习的变化检测方法进行全面分析总结，按照高光谱图像采集条件是否相同，将其分为同构高光谱图像变化检测以及异构高光谱图像变化检测。其中，从特征提取的网络结构优化和特征提取前混合像素处理两个角度，又将同构高光谱图像变化检测方法进一步分为基于时序依赖和空谱信息提取的方法以及基于端元提取和解混的方法，分别总结各类方法的特点和局限性，并讨论未来的研究重点。此外，面对不同传感器获取的异构高光谱图像数据，现有的方法基于图论学习图像的结构关系，并基于图像变换将其转换至公共域从而进行变化检测处理。本文从高光谱图像变化检测领域的新设计、新方法和应用场景出发，通过综合国内外前沿文献来梳理该领域的主要发展，重点论述高光谱图像变化检测领域的发展现状、前沿动态、热点问题及趋势。
Compared with natural and multispectral images， hyperspectral images contain rich spatial-spectral information. They not only can retain the spatial information of the target but also can obtain highly distinguishable spectral information and provide more detailed target feature information. Therefore， hyperspectral images have become one of the most commonly used data types for Earth observation and are widely used in important fields that actually affect human livelihoods， such as natural disaster monitoring， urban landscape mapping， agricultural surveys， environmental management， and homeland security. Change detection refers to the timely observation of land cover changes by analyzing images of the same geographical area and different phases. It is one of the important research directions in remote sensing image processing. With the increasing popularity and application of remote sensing technology， various remote sensing image processing technologies such as target detection and change detection have played an indispensable role in Earth observation tasks， such as natural disaster monitoring， agricultural surveys， environmental management， and homeland security. The important areas for exploration are as follows. Many problems and challenges still exist in the process of change detection in hyperspectral images. On the one hand， hyperspectral images have a large number of spectral bands， which leads to too high dimensions of data. This condition easily causes dimensionality disaster. Moreover， processing high-dimensional data requires a high degree of computing resources. Therefore， in real application scenarios， hyperspectral image change detection is difficult to achieve. Data processing and method design are often easily limited by device storage performance and computing performance. On the other hand， hyperspectral images acquired at different time points may have differences in spectral response due to factors such as illumination and atmospheric conditions. They may also have inconsistencies in resolution and spatial location， which increases the complexity of change detection. Spectral mixing is also one of the important challenges facing hyperspectral change detection. Pixels in hyperspectral data may contain a variety of ground object information， which seriously interferes with the accuracy of change detection. In addition， with the rapid development of hyperspectral remote sensing technology， equipment such as drones and satellites continue to collect a large number of hyperspectral images for use in important fields such as disaster monitoring， mineral exploration， and homeland security. However， differences exist in the shooting angle， height， and resolution of the sensors configured on different devices， and the acquired hyperspectral images also have distinctions in data structures. The existing isomorphism-based change detection methods for hyperspectral images cannot be directly applied to heterogeneous hyperspectral image change detection task. Therefore， the problem of change detection in heterogeneous hyperspectral images from different types of sensors also brings new challenges to hyperspectral image change detection. Therefore， alleviating the highly complex data characteristics of hyperspectral images and designing practical and effective processing solutions are important to improve the change detection effect. Among existing change detection methods for hyperspectral images， traditional methods usually use manually designed methods for feature extraction， which may fail to effectively extract discriminative features， and selecting appropriate thresholds to accurately detect land cover changes is difficult. Research on change detection technology for hyperspectral images has been greatly developed due to in-depth research on deep learning theory. Existing change detection methods based on deep learning can be roughly divided into two categories. One is based on temporal dependence and spatial spectrum information. The extraction method uses the temporal dependence and correlation between images to obtain more discriminative features and enhance the change detection performance. The other type is the method based on endmember extraction and unmixing， which is solved by the endmember extraction and unmixing method. Spectral mixing problem for hyperspectral images aims to improve change detection accuracy. In addition， in the face of heterogeneous hyperspectral image data acquired from different sensors such as drones and satellites， existing methods learn the structural relationships of heterogeneous images based on graph theory. They convert them into the public domain based on image transformation to make changes. With regard to detection and processing， certain temporal dependencies and correlations exist between multiple input images in the change detection task. Thus， effectively utilizing the temporal dependencies and correlations between images and combining the existing deep learning module design to improve the hyperspectral image change detection effect are also current issues. Regarding the research hotspots， this study starts from the new designs， new methods， and application scenarios in the field of hyperspectral image change detection， and the main developments in this field are sorted out by synthesizing domestic and foreign cutting-edge literature. First， it introduces the key preprocessing technologies for hyperspectral image change detection. Second， for the problem of isomorphic hyperspectral image change detection， traditional and deep learning-based methods for hyperspectral image change detection are introduced， and each type of method is systematically introduced and compared according to the different implementation methods. For heterogeneous hyperspectral image change detection and spectral image change detection problem， this study introduces and summarizes the existing heterogeneous change detection methods for hyperspectral images. Next， the current status of application of change detection methods for hyperspectral images in real industrial practice is discussed. Finally， the current research and development status at home and abroad is compared， and the development trend of hyperspectral image change detection is prospected.
高光谱图像变化检测时序特征提取端元解混异构高光谱图像变化检测
hyperspectral imagechange detectiontemporal feature extractionendmember unmixingheterogeneous hyperspectral image change detection
Baisantry M， Negi D S and Manocha O P. 2012. Change vector analysis using enhanced PCA and inverse triangular function-based thresholding. Defence Science Journal， 62（4）： 236-242 ［DOI： 10.14429/dsj.62.1072http://dx.doi.org/10.14429/dsj.62.1072］
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.2006.885408http://dx.doi.org/10.1109/TGRS.2006.885408］
Bovolo F， Bruzzone L and Marconcini M. 2008. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Transactions on Geoscience and Remote Sensing， 46（7）： 2070-2082 ［DOI： 10.1109/TGRS.2008.916643http://dx.doi.org/10.1109/TGRS.2008.916643］
Bovolo F， Marchesi S and Bruzzone L. 2012. A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing， 50（6）： 2196-2212 ［DOI： 10.1109/TGRS.2011.2171493http://dx.doi.org/10.1109/TGRS.2011.2171493］
Chen Z and Wang B. 2017. Spectrally-spatially regularized low-rank and sparse decomposition： a novel method for change detection in multitemporal hyperspectral images. Remote Sensing， 9（10）： #1044 ［DOI： 10.3390/rs9101044http://dx.doi.org/10.3390/rs9101044］
Chen Z H， Wang Y Y， Gao H M， Ding Y， Zhong Q Q， Hong D F and Zhang B. 2023. Temporal difference-guided network for hyperspectral image change detection. International Journal of Remote Sensing， 44（19）： 6033-6059 ［DOI： 10.1080/01431161.2023.2258563http://dx.doi.org/10.1080/01431161.2023.2258563］
Daudt R C， Le Saux B， Boulch A and Gousseau Y. 2018. Urban change detection for multispectral earth observation using convolutional neural networks//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia， Spain： IEEE： 2115-2118 ［DOI： 10.1109/IGARSS.2018.8518015http://dx.doi.org/10.1109/IGARSS.2018.8518015］
Dong W Q， Zhao J Y， Qu J H， Xiao S， Li N， Hou S X and Li Y S. 2023. Abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention for hyperspectral change detection. IEEE Transactions on Geoscience and Remote Sensing， 61： #5501513 ［DOI： 10.1109/TGRS.2023.3235401http://dx.doi.org/10.1109/TGRS.2023.3235401］
Du Q， Wasson L and King R. 2005. Unsupervised linear unmixing for change detection in multitemporal airborne hyperspectral imagery//Proceedings of 2005 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. Biloxi， USA： IEEE： 136-140 ［DOI： 10.1109/AMTRSI.2005.1469856http://dx.doi.org/10.1109/AMTRSI.2005.1469856］
Ertürk A， Iordache M D， and Plaza A. 2016. Sparse unmixing-based change detection for multitemporal hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 9（2）： 708-719 ［DOI： 10.1109/JSTARS.2015.2477431http://dx.doi.org/10.1109/JSTARS.2015.2477431］
Fan W， Zhou M and Huang R. 2020. Multiscale deep features fusion for change detection. Journal of Image and Graphics， 25（4）： 669-678
樊玮， 周末， 黄睿. 2020. 多尺度深度特征融合的变化检测. 中国图象图形学报， 25（4）： 669-678 ［DOI： 10.11834/jig.190312http://dx.doi.org/10.11834/jig.190312］
Frank M and Canty M. 2003. Unsupervised change detection for hyperspectral images//Proceedings of the 12th JPL Airborne Earth Science Workshop. Pasadena， USA： JPL Publication： 63-72
Guo Q L， Zhang J P and Zhang Y. 2021a. Multitemporal hyperspectral images change detection based on joint unmixing and information coguidance strategy. IEEE Transactions on Geoscience and Remote Sensing， 59（11）： 9633-9645 ［DOI： 10.1109/TGRS.2020.3045799http://dx.doi.org/10.1109/TGRS.2020.3045799］
Guo Q L， Zhang J P， Zhong C X and Zhang Y. 2021b. Change detection for hyperspectral images via convolutional sparse analysis and temporal spectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 4417-4426 ［DOI： 10.1109/JSTARS.2021.3074538http://dx.doi.org/10.1109/JSTARS.2021.3074538］
Hu M Q， Wu C and Zhang L P. 2022. HyperNet： self-supervised hyperspectral spatial-spectral feature understanding network for hyperspectral change detection. IEEE Transactions on Geoscience and Remote Sensing， 60， #5543017 ［DOI： 10.1109/TGRS.2022.3218795http://dx.doi.org/10.1109/TGRS.2022.3218795］
Hu M Q， Wu C， Du B and Zhang L P. 2023. Binary change guided hyperspectral multiclass change detection. IEEE Transactions on Image Processing， 32： 791-806 ［DOI： 10.1109/TIP.2022.3233187http://dx.doi.org/10.1109/TIP.2022.3233187］
Jafarzadeh H and Hasanlou M. 2019. An unsupervised binary and multiple change detection approach for hyperspectral imagery based on spectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 12（12）： 4888-4906 ［DOI： 10.1109/JSTARS.2019.2939133http://dx.doi.org/10.1109/JSTARS.2019.2939133］
Li H S， Wu K and Xu Y. 2022. An integrated change detection method based on spectral unmixing and the CNN for hyperspectral imagery. Remote Sensing， 14（11）： #2523 ［DOI： 10.3390/rs14112523http://dx.doi.org/10.3390/rs14112523］
Li Q X， Gong H， Dai H， Li C L， He Z P， Wang W J， Feng Y S， Han F， Tuniyazi A， Li H Y and Mu T K. 2021. Unsupervised hyperspectral image change detection via deep learning self-generated credible labels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 9012-9024 ［DOI： 10.1109/JSTARS.2021.3108777http://dx.doi.org/10.1109/JSTARS.2021.3108777］
Li X L， Yuan Z H and Wang Q. 2019. Unsupervised deep noise modeling for hyperspectral image change detection. Remote Sensing， 11（3）： #258 ［DOI： 10.3390/rs11030258http://dx.doi.org/10.3390/rs11030258］
Li X R and Ding J G. 2023. Spectral-temporal Transformer for hyperspectral image change detection. Remote Sensing， 15（14）： #3561 ［DOI： 10.3390/rs15143561http://dx.doi.org/10.3390/rs15143561］
Liu H F， Yang M H， Chen J， Hou J L and Deng M. 2018. Line-constrained shape feature for building change detection in VHR remote sensing imagery. ISPRS International Journal of Geo-Information， 7（10）： #410 ［DOI： 10.3390/ijgi7100410http://dx.doi.org/10.3390/ijgi7100410］
Liu H X， Meng X L， Jiang T， Liu X X and Zhang A B. 2016a. Change detection of phragmites australis distribution in the detroit wildlife refuge based on an iterative intersection analysis algorithm. Sustainability， 8（3）： #264 ［DOI： 10.3390/su8030264http://dx.doi.org/10.3390/su8030264］
Liu S C， Bruzzone L， Bovolo F and Du P J. 2014. A novel sequential spectral change vector analysis for representing and detecting multiple changes in hyperspectral images//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec， Canada： IEEE： 4656-4659 ［DOI： 10.1109/IGARSS.2014.6947531http://dx.doi.org/10.1109/IGARSS.2014.6947531］
Liu S C， Bruzzone L， Bovolo F and Du P J. 2015a. Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing， 53（1）： 244-260 ［DOI： 10.1109/TGRS.2014.2321277http://dx.doi.org/10.1109/TGRS.2014.2321277］
Liu S C， Bruzzone L， Bovolo F， Zanetti M and Du P J. 2015b. Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing， 53（8）： 4363-4378 ［DOI： 10.1109/TGRS.2015.2396686http://dx.doi.org/10.1109/TGRS.2015.2396686］
Liu S C， Bruzzone L， Bovolo F and Du P J. 2016b. Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing， 54（5）： 2733-2748 ［DOI： 10.1109/TGRS.2015.2505183http://dx.doi.org/10.1109/TGRS.2015.2505183］
Liu S C， Du Q， Tong X H， Samat A， Bruzzone L and Bovolo F. 2017. Multiscale morphological compressed change vector analysis for unsupervised multiple change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 10（9）： 4124-4137 ［DOI： 10.1109/JSTARS.2017.2712119http://dx.doi.org/10.1109/JSTARS.2017.2712119］
Lyu H B， Lu H and Mou L C. 2016. Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sensing， 8（6）： #506 ［DOI： 10.3390/rs8060506http://dx.doi.org/10.3390/rs8060506］
Lyu H B， Lu H， Mou L C， Li W Y， Wright J， Li X C， Li X L， Zhu X X， Wang J， Yu L and Gong P. 2018. Long-term annual mapping of four cities on different continents by applying a deep information learning method to landsat data. Remote Sensing， 10（3）： #471 ［DOI： 10.3390/rs10030471http://dx.doi.org/10.3390/rs10030471］
Lyu Z Y， Shi W Z， Zhou X C and Benediktsson J A. 2017. Semi-automatic system for land cover change detection using bi-temporal remote sensing images. Remote Sensing， 9（11）： #1112 ［DOI： 10.3390/rs9111112http://dx.doi.org/10.3390/rs9111112］
Mandanici E and Bitelli G. 2015. Multi-image and multi-sensor change detection for long-term monitoring of arid environments with Landsat series. Remote Sensing， 7（10）： 14019-14038 ［DOI： 10.3390/rs71014019http://dx.doi.org/10.3390/rs71014019］
Misbari S and Hashim M. 2016. Change detection of submerged seagrass biomass in shallow coastal water. Remote Sensing， 8（3）： #200 ［DOI： 10.3390/rs8030200http://dx.doi.org/10.3390/rs8030200］
Nielsen A A. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyper-spectral data. IEEE Transactions on Image Processing， 16（2）： 463-478 ［DOI： 10.1109/TIP.2006.888195http://dx.doi.org/10.1109/TIP.2006.888195］
Nielsen A A and Müller A. 2003. Change detection by the MAD method in hyperspectral image data//Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy. Herrsching， Germany： RAESeL： 115-116
Nourani V， Roushangar K and Andalib G. 2018. An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches. Journal of Hydrology， 562： 371-384 ［DOI： 10.1016/j.jhydrol.2018.05.018http://dx.doi.org/10.1016/j.jhydrol.2018.05.018］
Ortiz-Rivera V， Vélez-Reyes M and Roysam B. 2006. Change detection in hyperspectral imagery using temporal principal components//Proceedings of SPIE 6233， Algorithms and Technologies for Multispectral， Hyperspectral， and Ultraspectral Imagery XII. Orlando， USA： SPIE： #623312 ［DOI： 10.1117/12.667961http://dx.doi.org/10.1117/12.667961］
Qu J H， Xu Y S， Dong W Q， Li Y S and Du Q. 2021. Dual-branch difference amplification graph convolutional network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5519912 ［DOI： 10.1109/TGRS.2021.3135567http://dx.doi.org/10.1109/TGRS.2021.3135567］
Shafique A， Seydi S T， Alipour-Fard T， Cao G and Yang D. 2023. SSViT-HCD： a spatial-spectral convolutional vision Transformer for hyperspectral change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 16： 6487-6504 ［DOI： 10.1109/JSTARS.2023.3251646http://dx.doi.org/10.1109/JSTARS.2023.3251646］
Shi C J， Zhang Z J， Zhang W C， Zhang C R and Xu Q. 2022. Learning multiscale temporal-spatial-spectral features via a multipath convolutional LSTM neural network for change detection with hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing， 60： #5529816 ［DOI： 10.1109/TGRS.2022.3176642http://dx.doi.org/10.1109/TGRS.2022.3176642］
Song A， Choi J， Han Y and Kim Y. 2018. Change detection in hyperspectral images using recurrent 3D fully convolutional networks. Remote Sensing， 10（11）： #1827 ［DOI： 10.3390/rs10111827http://dx.doi.org/10.3390/rs10111827］
Song R， Ni W， Cheng W and Wang X. 2022. CSANet： cross-temporal interaction symmetric attention network for hyperspectral image change detection. IEEE Geoscience and Remote Sensing Letters， 19： #6010105 ［DOI： 10.1109/LGRS.2022.3179134http://dx.doi.org/10.1109/LGRS.2022.3179134］
Sublime J and Kalinicheva E. 2019. Automatic post-disaster damage mapping using deep-learning techniques for change detection： case study of the Tohoku tsunami. Remote Sensing， 11（9）： #1123 ［DOI： 10.3390/rs11091123http://dx.doi.org/10.3390/rs11091123］
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.2023.3294884http://dx.doi.org/10.1109/TGRS.2023.3294884］
Vongsy K and Mendenhall M J. 2016. Integrating spatial & spectral information for change detection in hyperspectral imagery//Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing： Evolution in Remote Sensing. Los Angeles， USA： IEEE： 1-5 ［DOI： 10.1109/WHISPERS.2016.8071703http://dx.doi.org/10.1109/WHISPERS.2016.8071703］
Wang L F， Wang L G， Wang Q M and Atkinson P M. 2021. SSA-SiamNet： spectral-spatial-wise attention-based Siamese network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5510018 ［DOI： 10.1109/TGRS.2021.3095899http://dx.doi.org/10.1109/TGRS.2021.3095899］
Wang L G， Wang L F， Wang Q M and Bruzzone L. 2022a. RSCNet： a residual self-calibrated network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5529917 ［DOI： 10.1109/TGRS.2022.3177478http://dx.doi.org/10.1109/TGRS.2022.3177478］
Wang Q， Yuan Z H， Du Q and Li X L. 2019. GETNET： a general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 57（1）： 3-13 ［DOI： 10.1109/TGRS.2018.2849692http://dx.doi.org/10.1109/TGRS.2018.2849692］
Wang X H， Ni W H， Feng Y N and Song L Y. 2023. AgF2Net： attention-guided feature fusion network for multitemporal hyperspectral image change detection. IEEE Geoscience and Remote Sensing Letters， 20： #5507805 ［DOI： 10.1109/LGRS.2023.3302469http://dx.doi.org/10.1109/LGRS.2023.3302469］
Wang Y H， Hong D F， Sha J J， Gao L R， Liu L， Zhang Y G and Rong X H. 2022b. Spectral-spatial-temporal Transformers for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5536814 ［DOI： 10.1109/TGRS.2022.3203075http://dx.doi.org/10.1109/TGRS.2022.3203075］
Wu C， Du B and Zhang L P. 2013. A subspace-based change detection method for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 6（2）： 815-830 ［DOI： 10.1109/JSTARS.2013.2241396http://dx.doi.org/10.1109/JSTARS.2013.2241396］
Xiao K W， Sun Y L and Lei L. 2022. Change alignment-based image transformation for unsupervised heterogeneous change detection. Remote Sensing， 14（21）： #5622 ［DOI： 10.3390/rs14215622http://dx.doi.org/10.3390/rs14215622］
Yang Y F， Qu J H， Xiao S， Dong W Q， Li Y S and Du Q. 2022. A deep multiscale pyramid network enhanced with spatial-spectral residual attention for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing， 60： #5525513 ［DOI： 10.1109/TGRS.2022.3161386http://dx.doi.org/10.1109/TGRS.2022.3161386］
Ye Z， Bai L and He M Y. 2021. Review of spatial-spectral feature extraction for hyperspectral image. Journal of Image and Graphics， 26（8）： 1737-1763
叶珍， 白璘， 何明一. 2021. 高光谱图像空谱特征提取综述. 中国图象图形学报， 26（8）： 1737-1763［DOI： 10.11834/jig.210198http://dx.doi.org/10.11834/jig.210198］
Zhan T M， Song B， Xu Y， Wan M H， Wang X， Yang G W and Wu Z B. 2021. SSCNN-S： a spectral-spatial convolution neural network with Siamese architecture for change detection. Remote Sensing， 13（5）： #895 ［DOI： 10.3390/rs13050895http://dx.doi.org/10.3390/rs13050895］
Zhang X R， Tian S L， Wang G C， Tang X， Feng J and Jiao L C. 2023. CAST： a CAscade spectral-aware Transformer for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing. 61： #5520714 ［DOI： 10.1109/TGRS.2023.3304681http://dx.doi.org/10.1109/TGRS.2023.3304681］
Zhao C H， Cheng H and Feng S. 2022. A spectral-spatial change detection method based on simplified 3-D convolutional autoencoder for multitemporal hyperspectral images. IEEE Geoscience and Remote Sensing Letters， 19： #5507705 ［DOI： 10.1109/LGRS.2021.3096526http://dx.doi.org/10.1109/LGRS.2021.3096526］
Zhou Z J， Ma L， Fu T Y， Zhang G， Yao M R and Li M C. 2018. Change detection in coral reef environment using high-resolution images： comparison of object-based and pixel-based paradigms. ISPRS International Journal of Geo-Information， 7（11）： #441 ［DOI： 10.3390/ijgi7110441http://dx.doi.org/10.3390/ijgi7110441］
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