人工智能时代的遥感变化检测技术：继承、发展与挑战

柳思聪; 都科丞; 郑永杰; 陈晋; 杜培军; 童小华

doi:10.11834/jrs.20222199

变化检测与深度学习 | 浏览量 : 0 下载量: 1729 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

人工智能时代的遥感变化检测技术：继承、发展与挑战
Remote sensing change detection technology in the Era of artificial intelligence： Inheritance， development and challenges
2023年27卷第9期页码：1975-1987
纸质出版日期： 2023-09-07 ，
DOI： 10.11834/jrs.20222199

扫描看全文

柳思聪，都科丞，郑永杰，陈晋，杜培军，童小华.2023.人工智能时代的遥感变化检测技术：继承、发展与挑战.遥感学报，27（9）： 1975-1987

Liu S C，Du K C，Zheng Y J，Chen J，Du P J and Tong X H. 2023. Remote sensing change detection technology in the Era of artificial intelligence： Inheritance， development and challenges. National Remote Sensing Bulletin， 27（9）：1975-1987
柳思聪，都科丞，郑永杰，陈晋，杜培军，童小华.2023.人工智能时代的遥感变化检测技术：继承、发展与挑战.遥感学报，27（9）： 1975-1987 DOI： 10.11834/jrs.20222199.

Liu S C，Du K C，Zheng Y J，Chen J，Du P J and Tong X H. 2023. Remote sensing change detection technology in the Era of artificial intelligence： Inheritance， development and challenges. National Remote Sensing Bulletin， 27（9）：1975-1987 DOI： 10.11834/jrs.20222199.

摘要

多时相遥感影像变化检测是指对同一地理区域、不同时间获取的遥感影像进行自动变化发现、识别与解释的遥感处理与分析技术。随着卫星遥感技术及人工智能理论方法的快速发展，基于多时相遥感影像数据驱动和模型驱动的传统变化检测方法正朝着数据—模型—知识联合驱动的方向转型和演变，以更加自动化、精细化和智能化的方式，解决多领域的地表时空变化检测问题。本文在总结多时相遥感数据源从同构到异构、变化检测模型从传统到智能、变化检测应用从理论到落地过程中存在问题的基础上，以光学遥感影像变化检测任务为例，梳理和分析了人工智能时代下变化检测技术的发展历程。从无监督、监督、弱监督3个方面探讨了遥感变化检测从传统到前沿技术的转型特点与趋势，并进一步提出了未来需重点突破模型的物理可解释性、泛化及迁移能力、跨数据—跨场景—跨领域应用水平等关键问题。

Abstract

In the past decades

the effects of global climate change and the increase of human activities have remarkably increased the demand for remote sensing monitoring. Moreover

with the accumulation of remote sensing data from multiple platforms and multiple sensors

the quantity and quality of multitemporal images have substantially improved. Multitemporal remote sensing images Change Detection (CD) is a processing and analysis technology that aims to automatically detect

identify

and describe changes occurring in the same geographical area at different times. With the advancement of remote sensing and Artificial Intelligence (AI) technology

traditional data-driven and modal CD methods are evolving toward data-model-knowledge jointly driven direction to solve the land surface spatio-temporal CD problem in a variety of application fields in a more automatic

refined

and intelligent manner. This paper first summarizes existing problems in multitemporal remote sensing CD by analyzing the use of homogeneous and heterogenous data sources

developments from traditional to intelligent CD models

and challenges from theoretical to practical CD applications. Optical image CD is taken as an example

and the evolution of CD technology in the era of AI is examined

which can be summarized as three periods of data-driven CD

model-driven CD

and data-model-knowledge driven CD. Then

the characteristics and problems of each periods are discussed. Furthermore

for each of the three aspects (unsupervised

supervised

and weakly supervised)

the characteristics and trends in the development of traditional to cutting-edge CD techniques are discussed. In the future

one can focus on breaking through key issues such as the physical interpretability

generalization

and transferability of the CD models as well as their successful implementation in cross-data

cross-scene

and cross-domain applications.

关键词

遥感变化检测多时相分析人工智能机器学习深度学习

Keywords

remote sensingchange detectionmulti-temporal analysisartificial intelligencemachine learningdeep learning

references

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, Camps-Valls G and Bruzzone L. 2010. A support vector domain method for change detection in multitemporal images. Pattern Recognition Letters, 31(10): 1148-1154 [DOI: 10.1016/j.patrec.2009.07.002http://dx.doi.org/10.1016/j.patrec.2009.07.002]

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]

Bruzzone L and Prieto D F. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182 [DOI: 10.1109/36.843009http://dx.doi.org/10.1109/36.843009]

Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317-321 [DOI: 10.1109/LGRS.2010.2068537http://dx.doi.org/10.1109/LGRS.2010.2068537]

Chen J, Gong P, He C Y, Pu R L and Shi P J. 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing, 69(4): 369-379 [DOI: 10.14358/PERS.69.4.369http://dx.doi.org/10.14358/PERS.69.4.369]

Chen X H, Chen J, Shi Y S and Yamaguchi Y. 2012. An automated approach for updating land cover maps based on integrated change detection and classification methods. ISPRS Journal of Photogrammetry and Remote Sensing, 71: 86-95 [DOI: 10.1016/j.isprsjprs.2012.05.006http://dx.doi.org/10.1016/j.isprsjprs.2012.05.006]

Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Review Article Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565-1596 [DOI: 10.1080/0143116031000101675http://dx.doi.org/10.1080/0143116031000101675]

Dalponte M, Jucker T, Liu S C, Frizzera L and Gianelle D. 2019. Characterizing forest carbon dynamics using multi-temporal lidar data. Remote Sensing of Environment, 224: 412-420 [DOI: 10.1016/j.rse.2019.02.018http://dx.doi.org/10.1016/j.rse.2019.02.018]

Demir B, Bovolo F and Bruzzone L. 2013. Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 300-312 [DOI: 10.1109/TGRS.2012.2195727http://dx.doi.org/10.1109/TGRS.2012.2195727]

Dian Y Y. 2005. Research on Change Detection based in Remote Sensing Imagery. Wuhan: Wuhan University

佃袁勇. 2005. 基于遥感影像的变化检测研究. 武汉: 武汉大学

Ding L, Guo H T, Liu S C, Mou L C, Zhang J and Bruzzone L. 2022. Bi-temporal semantic reasoning for the semantic change detection in hr remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5620014 [DOI: 10.1109/TGRS.2022.3154390http://dx.doi.org/10.1109/TGRS.2022.3154390]

Du B, Ru L X, Wu C and Zhang L P. 2019. Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9976-9992 [DOI: 10.1109/TGRS.2019.2930682http://dx.doi.org/10.1109/TGRS.2019.2930682]

Du P J and Liu S C. 2012c. Change detection from multi-temporal remote sensing images by integrating multiple features. Journal of Remote Sensing, 16(4): 663-677

杜培军, 柳思聪. 2012c. 融合多特征的遥感影像变化检测. 遥感学报, 16(4): 663-677 [DOI: 10.11834/jrs.20121168http://dx.doi.org/10.11834/jrs.20121168]

Du P J, Liu S C, Gamba P, Tan K and Xia J S. 2012. Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4): 1076-1086 [DOI: 10.1109/JSTARS.2012.2200879http://dx.doi.org/10.1109/JSTARS.2012.2200879]

Du P J, Liu S C, Liu P, Tan K and Cheng L. 2014. Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images. Geo-spatial Information Science, 17(1): 26-38 [DOI: 10.1080/10095020.2014.889268http://dx.doi.org/10.1080/10095020.2014.889268]

Du P J, Liu S C and Tan K. 2012b. Rapid monitoring of japan earthquake-triggered tsunami disaster based on a fusion of multiple features derived from HJ small satellite images. Science and Technology Review, 30(4): 31-36

杜培军, 柳思聪, 谭琨. 2012b. 国产HJ小卫星遥感影像多特征融合用于日本海啸灾情快速监测. 科技导报, 30(4): 31-36 [DOI: 10.3981/j.issn.1000-7857.2012.04.003http://dx.doi.org/10.3981/j.issn.1000-7857.2012.04.003]

Du P J, Liu S C, Xia J S and Zhao Y D. 2013. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14(1): 19-27 [DOI: 10.1016/j.inffus.2012.05.003http://dx.doi.org/10.1016/j.inffus.2012.05.003]

Du P J, Liu S C and Zheng H. 2012a. Land cover change detection over mining areas based on support vector machine. Journal of China University of Mining and Technology, 41(2): 262-267

杜培军, 柳思聪, 郑辉. 2012a. 基于支持向量机的矿区土地覆盖变化检测. 中国矿业大学学报, 41(2): 262-267

Feng W Q, Sui H G, Tu J H, Huang W M, Xu C and Sun K M. 2018. A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses. Remote Sensing, 10(7): 1015 [DOI: 10.3390/rs10071015http://dx.doi.org/10.3390/rs10071015]

Guo Q L, Zhang J P, Zhong C X and Zhang Y. 2021. 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]

Han Y and Byun Y. 2015. Automatic and accurate registration of VHR optical and SAR images using a quadtree structure. International Journal of Remote Sensing, 36(9): 2277-2295 [DOI: 10.1080/01431161.2015.1030046http://dx.doi.org/10.1080/01431161.2015.1030046]

Jiang F L, Gong M G, Zhan T and Fan X L. 2020. A semisupervised GAN-based multiple change detection framework in multi-spectral images. IEEE Geoscience and Remote Sensing Letters, 17(7): 1223-1227 [DOI: 10.1109/LGRS.2019.2941318http://dx.doi.org/10.1109/LGRS.2019.2941318]

Jiang X, Li G, Zhang X P and He Y. 2022. 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.2021.3061686http://dx.doi.org/10.1109/TGRS.2021.3061686]

Kasetkasem T and Varshney P K. 2002. An image change detection algorithm based on Markov random field models. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1815-1823 [DOI: 10.1109/TGRS.2002.802498http://dx.doi.org/10.1109/TGRS.2002.802498]

Khelifi L and Mignotte M. 2020. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis. IEEE Access, 8: 126385-126400 [DOI: 10.1109/ACCESS.2020.3008036http://dx.doi.org/10.1109/ACCESS.2020.3008036]

Li S T, Li C Y and Kang X D. 2021. Development status and future prospects of multi-source remote sensing image fusion. National Remote Sensing Bulletin, 25(1): 148-166

李树涛, 李聪妤, 康旭东. 2021. 多源遥感图像融合发展现状与未来展望. 遥感学报, 25(1): 148-166 [DOI: 10.11834/jrs.20210259http://dx.doi.org/10.11834/jrs.20210259]

Liu S C, Bovolo F, Bruzzone L, Du Q and Tong X H. 2021. Unsupervised change detection in multitemporal remote sensing images//Change Detection and Image Time Series Analysis 1: Unsupervised Methods. [s.l.]: John Wiley & Sons: 1-34 [DOI: 10.1002/9781119882268.ch1http://dx.doi.org/10.1002/9781119882268.ch1]

Liu S C, Bruzzone L, Bovolo F and Du P J. 2012. Unsupervised hierarchical spectral analysis for change detection in hyperspectral images//2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Shanghai: IEEE: 1-4 [DOI: 10.1109/WHISPERS.2012.6874245http://dx.doi.org/10.1109/WHISPERS.2012.6874245]

Liu S C, Bruzzone L, Bovolo F and Du P J. 2015b. 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 and Du P J. 2016. 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, Bruzzone L, Bovolo F, Zanetti M and Du P J. 2015a. 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, Chi M M, Zou Y X, Samat A, Benediktsson J A and Plaza A. 2017a. Oil spill detection via multitemporal optical remote sensing images: a change detection perspective. IEEE Geoscience and Remote Sensing Letters, 14(3): 324-328 [DOI: 10.1109/LGRS.2016.2639540http://dx.doi.org/10.1109/LGRS.2016.2639540]

Liu S C, Du Q, Tong X H, Samat A and Bruzzone L. 2019a. Unsupervised change detection in multispectral remote sensing images via spectral-spatial band expansion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3578-3587 [DOI: 10.1109/JSTARS.2019.2929514http://dx.doi.org/10.1109/JSTARS.2019.2929514]

Liu S C, Du Q, Tong X H, Samat A, Bruzzone L and Bovolo F. 2017b. 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]

Liu S C, Du Q, Tong X H, Samat A, Pan H Y and Ma X L. 2017c. Band selection-based dimensionality reduction for change detection in multi-temporal hyperspectral images. Remote Sensing, 9(10): 1008 [DOI: 10.3390/rs9101008http://dx.doi.org/10.3390/rs9101008]

Liu S C, Marinelli D, Bruzzone L and Bovolo F. 2019b. A review of change detection in multitemporal hyperspectral images: current techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(2): 140-158 [DOI: 10.1109/MGRS.2019.2898520http://dx.doi.org/10.1109/MGRS.2019.2898520]

Liu S C, Tong X H, Bruzzone L and Du P J. 2017d. A novel semisupervised framework for multiple change detection in hyperspectral images//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth: IEEE: 173-176 [DOI: 10.1109/IGARSS.2017.8126922http://dx.doi.org/10.1109/IGARSS.2017.8126922]

Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2401 [DOI: 10.1080/0143116031000139863http://dx.doi.org/10.1080/0143116031000139863]

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.2019.2930348http://dx.doi.org/10.1109/TGRS.2019.2930348]

Lv Z Y, Liu T F, Shi C, Benediktsson J A and Du H J. 2019. Novel land cover change detection method based on k-means clustering and adaptive majority voting using bitemporal remote sensing images. IEEE Access, 7: 34425-34437 [DOI: 10.1109/ACCESS.2019.2892648http://dx.doi.org/10.1109/ACCESS.2019.2892648]

Nemmour H and Chibani Y. 2006. Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2): 125-133 [DOI: 10.1016/j.isprsjprs.2006.09.004http://dx.doi.org/10.1016/j.isprsjprs.2006.09.004]

Nielsen A A, Conradsen K and Simpson J J. 1998. Multivariate Alteration Detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote Sensing of Environment, 64(1): 1-19 [DOI: 10.1016/S0034-4257(97)00162-4http://dx.doi.org/10.1016/S0034-4257(97)00162-4]

Nielsen A A. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2): 463-478 [DOI: 10.1109/TIP.2006.888195http://dx.doi.org/10.1109/TIP.2006.888195]

Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]

Peng D F, Bruzzone L, Zhang Y J, Guan H Y, Ding H Y and Huang X. 2021. SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 5891-5906 [DOI: 10.1109/TGRS.2020.3011913http://dx.doi.org/10.1109/TGRS.2020.3011913]

Peng D F, Zhang Y J and Guan H Y. 2019. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11): 1382 [DOI: 10.3390/rs11111382http://dx.doi.org/10.3390/rs11111382]

Roy M, Ghosh S and Ghosh A. 2014. A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Information Sciences, 269: 35-47 [DOI: 10.1016/j.ins.2014.01.037http://dx.doi.org/10.1016/j.ins.2014.01.037]

Saha S, Bovolo F and Bruzzone L. 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 3677-3693 [DOI: 10.1109/TGRS.2018.2886643http://dx.doi.org/10.1109/TGRS.2018.2886643]

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/rs140408-71http://dx.doi.org/10.3390/rs140408-71]

Shi Q, Liu M X, Li S C, Liu X P, Wang F and Zhang L P. 2022. A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5604816 [DOI: 10.1109/TGRS.2021.3085870http://dx.doi.org/10.1109/TGRS.2021.3085870]

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/rs12101688http://dx.doi.org/10.3390/rs12101688]

Singh A. 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6): 989-1003 [DOI: 10.1080/01431168908903939http://dx.doi.org/10.1080/01431168908903939]

Soares V P and Hoffer R M. 1995. Eucalyptus forest change classification using multi-date Landsat TM data//Proceedings Volume 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources. Rome: SPIE [DOI: 10.1117/12.200769http://dx.doi.org/10.1117/12.200769]

Song X P, Hansen M C, Stehman S V, Potapov P V, Tyukavina A, Vermote E F and Townshend J R. 2018. Global land change from 1982 to 2016. Nature, 560(7720): 639-643 [DOI: 10.1038/s41586-018-0411-9http://dx.doi.org/10.1038/s41586-018-0411-9]

Sui H G, Feng W Q, Li W Z, Sun K M and Xu C. 2018. Review of change detection methods for multi-temporal remote sensing imagery. Geomatics and Information Science of Wuhan University, 43(12): 1885-1898

眭海刚, 冯文卿, 李文卓, 孙开敏, 徐川. 2018. 多时相遥感影像变化检测方法综述. 武汉大学学报(信息科学版), 43(12): 1885-1898 [DOI: 10.13203/j.whugis20180251http://dx.doi.org/10.13203/j.whugis20180251]

Tan K, Zhang Y S, Wang X and Chen Y. 2019. Object-based change detection using multiple classifiers and multi-scale uncertainty analysis. Remote Sensing, 11(3): 359 [DOI: 10.3390/rs11030359http://dx.doi.org/10.3390/rs11030359]

Tang X, Zhang H Y, Mou L C, Liu F, Zhang X R, Zhu X X and Jiao L C. 2022. An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning. IEEE Transactions on Geoscience and Remote Sensing, 60: 5609715 [DOI: 10.1109/TGRS.2021.3106381http://dx.doi.org/10.1109/TGRS.2021.3106381]

Tong G F, Li Y, Ding W L and Yue X Y. 2015. Review of remote sensing image change detection. Journal of Image and Graphics, 20(12): 1561-1571

佟国峰, 李勇, 丁伟利, 岳晓阳. 2015. 遥感影像变化检测算法综述. 中国图象图形学报, 20(12): 1561-1571 [DOI: 10.11834/jig.20151201http://dx.doi.org/10.11834/jig.20151201]

Tong X H, Pan H Y, Liu S C, Li B B, Luo X, Xie H and Xu X. 2020. A novel approach for hyperspectral change detection based on uncertain area analysis and improved transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 2056-2069 [DOI: 10.1109/JSTARS.2020.2990481http://dx.doi.org/10.1109/JSTARS.2020.2990481]

Volpi M, Tuia D, Bovolo F, Kanevski M and Bruzzone L. 2013. Supervised change detection in VHR images using contextual information and support vector machines. International Journal of Applied Earth Observation and Geoinformation, 20: 77-85 [DOI: 10.1016/j.jag.2011.10.013http://dx.doi.org/10.1016/j.jag.2011.10.013]

Wang L F, Wang L G, Wang Q M and Atkinson P M. 2022. 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 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, Du P J, Chen D M, Liu S C, Zhang W and Li E Z. 2020. Change detection based on low-level to high-level features integration with limited samples. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 6260-6276 [DOI: 10.1109/JSTARS.2020.3029460http://dx.doi.org/10.1109/JSTARS.2020.3029460]

Wang X, Liu S C, Du P J, Liang H, Xia J S and Li Y F. 2018. Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sensing, 10(2): 276 [DOI: 10.3390/rs10020276http://dx.doi.org/10.3390/rs10020276]

Wen D W, Huang X, Bovolo F, Li J Y, Ke X L, Zhang A L and Benediktsson J A. 2021. Change detection from very-high-spatial-resolution optical remote sensing images: methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine, 9(4): 68-101 [DOI: 10.1109/MGRS.2021.3063465http://dx.doi.org/10.1109/MGRS.2021.3063465]

Wu C, Du B and Zhang L P. 2014. Slow feature analysis for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2858-2874 [DOI: 10.1109/TGRS.2013.2266673http://dx.doi.org/10.1109/TGRS.2013.2266673]

Wu C, Zhang L P and Du B. 2017. Kernel slow feature analysis for scene change detection. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2367-2384 [DOI: 10.1109/TGRS.2016.2642125http://dx.doi.org/10.1109/TGRS.2016.2642125]

Yang M J, Jiao L C, Liu F, Hou B and Yang S Y. 2019. Transferred deep learning-based change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57 (9): 6960-6973 [DOI: 10.1109/TGRS.2019.2909781http://dx.doi.org/10.1109/TGRS.2019.2909781]

Zanetti M, Bovolo F and Bruzzone L. 2015. Rayleigh-rice mixture parameter estimation via EM algorithm for change detection in multispectral images. IEEE Transactions on Image Processing, 24(12): 5004-5016 [DOI: 10.1109/TIP.2015.2474710http://dx.doi.org/10.1109/TIP.2015.2474710]

Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459

张良培, 武辰. 2017. 多时相遥感影像变化检测的现状与展望. 测绘学报, 46(10): 1447-1459 [DOI: 10.11947/j.AGCS.2017.20170340http://dx.doi.org/10.11947/j.AGCS.2017.20170340]

文章被引用时，请邮件提醒。

提交

光学信号Token引导的异源遥感变化检测网络

深度学习的遥感变化检测综述：文献计量与分析

面向高分辨率遥感影像变化检测的混合空间金字塔池化网络