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Geo-spatial Information Science Volume 26, 2023 - Issue 3
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Research Article

Deep learning for change detection in remote sensing: a review

Ting Baia Project Planning Department, Wuhan Land Arranging Storage Center, Wuhan, ChinaORCID Iconhttps://orcid.org/0000-0002-7990-5405View further author information
ORCID Icon,
Le Wangb Department of Geography, University at Buffalo, The State University of New York, Buffalo, USACorrespondence[email protected]
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,
Dameng Yinc Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, ChinaORCID Iconhttps://orcid.org/0000-0001-9668-2744View further author information
ORCID Icon,
Kaimin Sund State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaORCID Iconhttps://orcid.org/0000-0002-2664-9479View further author information
ORCID Icon,
Yepei Chene School of Computer Science, Hubei University of Technology, Wuhan, ChinaORCID Iconhttps://orcid.org/0000-0002-2877-5065View further author information
ORCID Icon,
Wenzhuo Lid State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaORCID Iconhttps://orcid.org/0000-0002-2920-3202View further author information
ORCID Icon &
Deren Lid State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;f Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, ChinaView further author information
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Pages 262-288 | Received 19 Aug 2021, Accepted 30 May 2022, Published online: 01 Jul 2022

References

  • Amit, S.N.K.B., and Y. Aoki. 2017. “Disaster Detection from Aerial Imagery with Convolutional Neural Network.” 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 239–245. doi:10.1109/KCIC.2017.8228593.
  • Arabi, M.E.A., M.S. Karoui, and K. Djerriri. 2018. “Optical Remote Sensing Change Detection Through Deep Siamese Network.” In IGARSS 2018 - 2018 Ieee International Geoscience and Remote Sensing Symposium, 5041–5044. doi:10.1109/IGARSS.2018.8518178.
  • Argyridis, A., and D.P. Argialas. 2016. “Building Change Detection Through Multi-Scale GEOBIA Approach by Integrating Deep Belief Networks with Fuzzy Ontologies.” International Journal of Image and Data Fusion 7 (2): 148–171. doi:10.1080/19479832.2016.1158211.
  • Ball, J.E., D.T. Anderson, and C.S. Chan. 2018. “Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications.” Journal of Applied Remote Sensing 11 (4): 042601. doi:10.1117/1.JRS.11.042601.
  • Bazi, Y., M.M. Rahhal, H. Alhichri, and N. Alajlan. 2019. “Simple Yet Effective Fine-Tuning of Deep Cnns Using an Auxiliary Classification Loss for Remote Sensing Scene Classification.” Remote Sensing 11 (24): 2908. doi:10.3390/rs11242908.
  • Bengio, Y., P. Simard, and P. Frasconi. 1994. “Learning Long-Term Dependencies with Gradient Descent is Difficult.” IEEE Transactions on Neural Networks 5 (2): 157–166. doi:10.1109/72.279181.
  • Bovolo, F., L. Bruzzone, and M. Marconcini. 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.2008.916643.
  • Cao, G., Y. Li, Y. Liu, and Y. Shang. 2014. “Automatic Change Detection in High-Resolution Remote-Sensing Images by Means of Level Set Evolution and Support Vector Machine Classification.” International Journal of Remote Sensing 35 (16): 6255–6270. doi:10.1080/01431161.2014.951740.
  • Cao, G., B. Wang, H.C. Xavier, D. Yang, and J. Southworth. 2017. “A New Difference Image Creation Method Based on Deep Neural Networks for Change Detection in Remote-Sensing Images.” International Journal of Remote Sensing 38 (23): 7161–7175. doi:10.1080/01431161.2017.1371861.
  • Cao, C., S. Dragićević, and S. Li. 2019. “Land-Use Change Detection with Convolutional Neural Network Methods.” Environments 6 (2): 25. doi:10.3390/environments6020025.
  • Chan, J.C.W., K.P. Chan, and A.G.O. Yeh. 2001. “Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms.” Photogrammetric Engineering and Remote Sensing 67 (2): 213–226. doi:10.1144/petgeo.7.1.81.
  • Chehata, N., C. Orny, S. Boukir, D. Guyon, and J.P. Wigneron. 2014. “Object-Based Change Detection in Wind Storm-Damaged Forest Using High-Resolution Multispectral Images.” International Journal of Remote Sensing 35 (13): 4758–4777. doi:10.1080/01431161.2014.930199.
  • Chen, J., P. Gong, C. He, R. Pu, and P. Shi. 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.69.4.369.Chen.
  • Chen, F., J. Shi, and M. Gong. 2016. “Differencing Neural Network for Change Detection in Synthetic Aperture Radar Images.” International Conference on Bio-Inspired Computing: Theories and Applications 431–437. doi:10.1007/978-981-10-3611-8_38.Chen.
  • Chen, Z., Y. Zhang, C. Ouyang, F. Zhang, and J. Ma 2018. “Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.” Sensors 18 (3): 821. doi:10.3390/s18030821.
  • Chen, H., L. Jiao, M. Liang, F. Liu, S. Yang, and B. Hou 2019. “Fast Unsupervised Deep Fusion Network for Change Detection of Multitemporal SAR Images.” Neurocomputing 332: 56–70. doi:10.1016/j.neucom.2018.11.077.
  • Chen, L., F. Rottensteiner, and C. Heipke. 2021. “Feature Detection and Description for Image Matching: From Hand-Crafted Design to Deep Learning.” Geo-Spatial Information Science 24 (1): 58–74. doi:10.1080/10095020.2020.1843376.
  • Cheng, G., X. Xie, J. Han, L. Guo, and G.S. Xia. 2020. “Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 3735–3756. doi:10.1109/JSTARS.2020.3005403.
  • Chu, Y., G. Cao, and H. Hayat. 2016. “Change Detection of Remote Sensing Image Based on Deep Neural Networks.” Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering 133 (1): 262–267. doi:10.2991/aiie-16.2016.61.
  • Cireşan, D.C., U. Meier, L.M. Gambardella, and J. Schmidhuber. 2010. “Deep, Big, Simple Neural Nets for Handwritten Digit Recognition.” Neural Computation 22 (12): 3207–3220. doi:10.1162/NECO_a_00052.
  • Connors, C., and R.R. Vatsavai. 2017. “Semi-Supervised Deep Generative Models for Change Detection in Very High Resolution Imagery.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1063–1066. doi:10.1109/IGARSS.2017.8127139.
  • Dai, X.L., and S. Khorram. 1999. “Remotely Sensed Change Detection Based on Artificial Neural Networks.” Photogrammetric Engineering and Remote Sensing 65 (10): 1187–1194. doi:10.1117/1.482717.
  • De, S., D. Pirrone, F. Bovolo, L. Bruzzone, and A. Bhattacharya. 2017. “A Novel Change Detection Framework Based on Deep Learning for the Analysis of Multi-Temporal Polarimetric Sar Images.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 5193–5196. doi:10.1109/IGARSS.2017.8128171.
  • De Felice, M. 2017. “Which Deep Learning Network is Best for You? IDG Communications.” accessed May 4 20 1 7. http://www.cio.com/article/3193689/artificial-intelligence/which-deep-learning-network-is-best-for-you.html
  • El Amin, A.M., Q. Liu, and Y. Wang. 2016. “Convolutional Neural Network Features Based Change Detection in Satellite Images.” First International Workshop on Pattern Recognition, no. 10011: 181–186. doi:10.1117/12.2243798.
  • El Amin, A.M., Q. Liu, and Y. Wang. 2017. “Zoom Out Cnns Features for Optical Remote Sensing Change Detection.” In 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 812–817. doi:10.1109/ICIVC.2017.7984667.
  • Gao, F., X. Liu, J. Dong, G. Zhong, and M. Jian. 2017. “Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks.” Remote Sensing 9 (5): 435. doi:10.3390/rs9050435.
  • Gao, F., X. Wang, Y. Gao, J. Dong, and S. Wang. 2019a. “Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks.” IEEE Geoscience and Remote Sensing Letters 16 (8): 1240–1244. doi:10.1109/LGRS.2019.2895656.
  • Gao, Y., F. Gao, J. Dong, and S. Wang. 2019b. “Transferred Deep Learning for Sea Ice Change Detection from Synthetic-Aperture Radar Images.” IEEE Geoscience Remote Sensing Letters 16 (10): 1655–1659. doi:10.1109/LGRS.2019.2906279.
  • Gao, Y., J. Shi, J. Li, and R. Wang. 2021. “Remote Sensing Scene Classification Based on High-Order Graph Convolutional Network.” European Journal of Remote Sensing 54 (sup1): 141–155. doi:10.1080/22797254.2020.1868273.
  • Geng, J., H. Wang, J. Fan, and X. Ma. 2017. “Change Detection of SAR Images Based on Supervised Contractive Autoencoders and Fuzzy Clustering.” In 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), 1–3. doi:10.1109/RSIP.2017.7958819.
  • Gong, P. 1993. “Change Detection Using Principal Component Analysis and Fuzzy Set Theory.” Canadian Journal of Remote Sensing 19 (1): 22–29. doi:10.1080/07038992.1993.10855147.
  • Gong, M., J. Zhao, J. Liu, Q. Miao, and L. Jiao. 2015. “Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems 27 (1): 125–138. doi:10.1109/TNNLS.2015.2435783.
  • Gong, M., H. Yang, and P. Zhang. 2017. “Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images.” ISPRS Journal of Photogrammetry and Remote Sensing 129: 212–225. doi:10.1016/j.isprsjprs.2017.05.001.
  • Gong, M., X. Niu, P. Zhang, and Z. Li. 2017a. “Generative Adversarial Networks for Change Detection in Multispectral Imagery.” IEEE Geoscience and Remote Sensing Letters 14 (12): 2310–2314. doi:10.1109/Lgrs.2017.2762694.
  • Gong, M., T. Zhan, P. Zhang, and Q. Miao. 2017b. “Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 55 (5): 2658–2673. doi:10.1109/TGRS.2017.2650198.
  • Gong, M., X. Niu, T. Zhan, and M. Zhang. 2019a. “A Coupling Translation Network for Change Detection in Heterogeneous Images.” International Journal of Remote Sensing 40 (9): 3647–3672. doi:10.1080/01431161.2018.1547934.
  • Gong, M., Y. Yang, T. Zhan, X. Niu, and S. Li. 2019b. “A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (1): 321–333. doi:10.1109/JSTARS.2018.2887108.
  • Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.” Advances in Neural Information Processing Systems 27. doi:10.5555/2969033.2969125.
  • Han, J., X. Meng, X. Zhou, B. Yi, M. Liu, and W.N. Xiang. 2017a. “A Long-Term Analysis of Urbanization Process, Landscape Change, and Carbon Sources and Sinks: A Case Study in China’s Yangtze River Delta Region.” Journal of Cleaner Production 141: 1040–1050. doi:10.1016/j.jclepro.2016.09.177.
  • Han, X., Y. Zhong, L. Cao, and L. Zhang. 2017b. “Pre-Trained Alexnet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification.” Remote Sensing 9 (8): 848. doi:10.3390/rs9080848.
  • Healey, S.P., W.B. Cohen, Z. Yang, C.K. Brewer, E.B. Brooks, N. Gorelick, A.J. Hernandez, et al. 2018. ”Mapping Forest Change Using Stacked Generalization: An Ensemble Approach”. Remote Sensing of Environment 204: 717–728. 10.1016/j.rse.2017.09.029.
  • Hinton, G.E., S. Osindero, and Y.W. Teh. 2006. “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation 18 (7): 1527–1554. doi:10.1162/neco.2006.18.7.1527.
  • Hinton, G.E., and R.R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–507. doi:10.1126/science.1127647.
  • Hong, D., N. Yokoya, J. Chanussot, and X.X. Zhu. 2018. “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.” IEEE Transactions on Image Processing 28 (4): 1923–1938. doi:10.1109/TIP.2018.2878958.
  • Hong, D., L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot. 2020a. “Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (7): 5966–5978. doi:10.1109/TGRS.2020.3015157.
  • Hong, D., L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, and B. Zhang. 2020b. “More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (5): 4340–4354. doi:10.1109/TGRS.2020.3016820.
  • Hong, D., Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza, and J. Chanussot. 2021. “SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–15. doi:10.1109/TGRS.2021.3130716.
  • Howarth, P.J., and G.M. Wickware. 1981. “Procedures for Change Detection Using Landsat Digital Data.” International Journal of Remote Sensing 2 (3): 277–291. doi:10.1080/01431168108948362.
  • Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches.” ISPRS Journal of Photogrammetry and Remote Sensing 80: 91–106. doi:10.1016/j.isprsjprs.2013.03.006.
  • Iino, S., R. Ito, K. Doi, T. Imaizumi, and S. Hikosaka. 2018. “CNN-Based Generation of High-Accuracy Urban Distribution Maps Utilising SAR Satellite Imagery for Short-Term Change Monitoring.” International Journal of Image and Data Fusion 9 (4): 302–318. doi:10.1080/19479832.2018.1491897.
  • Jiao, L., L. Huo, C. Hu, and P. Tang. 2020. “Refined Unet: Unet-Based Refinement Network for Cloud and Shadow Precise Segmentation.” Remote Sensing 12 (12): 2001. doi:10.3390/rs12122001.
  • Johnson, R.D., and E.S. Kasischke. 1998. “Change Vector Analysis: A Technique for the Multispectral Monitoring of Land Cover and Condition.” International Journal of Remote Sensing 19 (3): 411–426. doi:10.1080/014311698216062.
  • Kemker, R., C. Salvaggio, and C. Kanan. 2018. “Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery Using Deep Learning.” ISPRS Journal of Photogrammetry and Remote Sensing 145: 60–77. doi:10.1016/j.isprsjprs.2018.04.014.
  • Khan, S.H., X. He, F. Porikli, and M. Bennamoun. 2017. “Forest Change Detection in Incomplete Satellite Images with Deep Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 55 (9): 5407–5423. doi:10.1109/TGRS.2017.2707528.
  • Khelifi, L., and M. Mignotte. 2020. “Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis.” IEEE Access 8: 126385–126400. doi:10.1109/ACCESS.2020.3008036.
  • LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. doi:10.1109/5.726791.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
  • Lei, T., Q. Zhang, D. Xue, T. Chen, H. Meng, and A.K. Nandi. 2019a. “End-To-End Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping.” In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3027–3031. doi: 10.1109/ICASSP.2019.8682802.
  • Lei, Y., X. Liu, J. Shi, C. Lei, and J. Wang. 2019b. “Multiscale Superpixel Segmentation with Deep Features for Change Detection.” IEEE Access 7: 36600–36616. doi:10.1109/ACCESS.2019.2902613.
  • Li, Y., L. Zhou, G. Lu, B. Hou, and L. Jiao. 2017. “Change Detection in Synthetic Aperture Radar Images Based on Log-Mean Operator and Stacked Auto-Encoder.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3090–3306. doi:10.1109/IGARSS.2017.8127652.
  • Li, Y., L. Xu, and T. Liu. 2018. “Unsupervised Change Detection for Remote Sensing Images Based on Object-Based MRF and Stacked Autoencoders.” 2016 International Conference on Orange Technologies (ICOT), 64–67. doi:10.1109/ICOT.2016.8278980.
  • Li, X., Z. Yuan, and Q. Wang. 2019. “Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection.” Remote Sensing 11 (3): 258. doi:10.3390/rs11030258.
  • Li, Y., C. Peng, Y. Chen, L. Jiao, L. Zhou, and R. Shang. 2019. “A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 5751–5763. doi:10.1109/Tgrs.2019.2901945.
  • Li, R., S. Zheng, C. Duan, L. Wang, and C. Zhang. 2022. “Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network.” Geo-Spatial Information Science 1–17. doi:10.1080/10095020.2021.2017237.
  • Lillesand, T., R.W. Kiefer, and J. Chipman. 2015. Remote Sensing and Image Interpretation. New York, NJ, USA: John Wiley & Sons.
  • Liu, S., L. Bruzzone, F. Bovolo, and P. Du. 2014. “Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (1): 244–260. doi:10.1109/TGRS.2014.2321277.
  • Liu, J., M. Gong, K. Qin, and P. Zhang. 2016a. “A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.” IEEE Transactions on Neural Networks and Learning Systems 29 (3): 545–559. doi:10.1109/TNNLS.2016.2636227.
  • Liu, J., M. Gong, J. Zhao, H. Li, and L. Jiao. 2016b. “Difference Representation Learning Using Stacked Restricted Boltzmann Machines for Change Detection in SAR Images.” Soft Computing 20 (12): 4645–4657. doi:10.1007/s00500-014-1460-0.
  • Liu, T., Y. Li, Y. Cao, and Q. Shen. 2017. “Change Detection in Multitemporal Synthetic Aperture Radar Images Using Dual-Channel Convolutional Neural Network.” Journal of Applied Remote Sensing 11 (4): 042615. doi:10.1117/1.JRS.11.042615.
  • Liu, J., M. Gong, K. Qin, and P. Zhang. 2018. “A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.” IEEE Transactions on Neural Networks and Learning Systems 29 (3): 545–559.
  • Liu, T., L. Yang, and D. Lunga. 2021. “Change Detection Using Deep Learning Approach with Object-Based Image Analysis.” Remote Sensing of Environment 256: 112308. doi:10.1016/j.rse.2021.112308.
  • Lu, D., P. Mausel, E. Brondizio, and E. Moran. 2004. “Change Detection Techniques.” International Journal of Remote Sensing 25 (12): 2365–2401. doi:10.1080/0143116031000139863.
  • Luus, F.P., B.P. Salmon, F. Van den Bergh, and B.T.J. Maharaj. 2015. “Multiview Deep Learning for Land-Use Classification.” IEEE Geoscience and Remote Sensing Letters 12 (12): 2448–2452. doi:10.1109/LGRS.2015.2483680.
  • Lyu, H., H. Lu, and L. Mou. 2016. “Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection.” Remote Sensing 8 (6): 506. doi:10.3390/rs8060506.
  • Lyu, H., and H. Lu. 2017. “A Deep Information Based Transfer Learning Method to Detect Annual Urban Dynamics of Beijing and New York from 1984–2016.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1958–1961. doi:10.1109/IGARSS.2017.8127363.
  • Ma, L., Y. Liu, X. Zhang, Y. Ye, G. Yin, and B.A. Johnson. 2019a. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152: 166–177. doi:10.1016/j.isprsjprs.2019.04.015.
  • Ma, W., Y. Xiong, Y. Wu, H. Yang, X. Zhang, and L. Jiao. 2019b. “Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network.” Remote Sensing 11 (6): 626. doi:10.3390/rs11060626.
  • McDermid, G.J., J. Linke, A.D. Pape, D.N. Laskin, A.J. McLane, and S.E. Franklin. 2008. “Object-Based Approaches to Change Analysis and Thematic Map Update: Challenges and Limitations.” Canadian Journal of Remote Sensing 34 (5): 462–466. doi:10.5589/m08-061.
  • Mercier, G., G. Moser, and S.B. Serpico. 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.
  • Mitchell, T., W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, et al. 2018. ”Never-Ending Learning.” Communications of the ACM 61 (5): 103–115. DOI:10.1145/3191513.
  • Mou, L., L. Bruzzone, and X. Zhu. 2018. “Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 924–935. doi:10.1109/TGRS.2018.2863224.
  • Mou, L., and X. Zhu. 2018. “A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images.” In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 4363–4366. doi:10.1109/IGARSS.2018.8517375.
  • Murray, H., A. Lucieer, and R. Williams. 2010. “Texture-Based Classification of Sub-Antarctic Vegetation Communities on Heard Island.” International Journal of Applied Earth Observation and Geoinformation 12 (3): 138–149. doi:10.1016/j.jag.2010.01.006.
  • Nemoto, K., R. Hamaguchi, M. Sato, A. Fujita, T. Imaizumi, and S. Hikosaka. 2017. “Building Change Detection via a Combination of Cnns Using Only RGB Aerial Imageries.” Remote Sensing Technologies and Applications in Urban Environments II 10431: 104310J. doi:10.1117/12.2277912.
  • Newbold, T., L.N. Hudson, S.L. Hill, S. Contu, I. Lysenko, R.A. Senior, L. Börger, et al. 2015. ”Global Effects of Land Use on Local Terrestrial Biodiversity.” Nature 520 (7545): 45–50. doi:10.1038/nature14324.
  • Prendes, J., M. Chabert, F. Pascal, A. Giros, and J.Y. Tourneret. 2015a. “Change Detection for Optical and Radar Images Using a Bayesian Nonparametric Model Coupled with a Markov Random Field.” In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1513–1517. doi:10.1109/ICASSP.2015.7178223.
  • Prendes, J., M. Chabert, F. Pascal, A. Giros, and J.Y. Tourneret. 2015b. “Performance Assessment of a Recent Change Detection Method for Homogeneous and Heterogeneous Images.” Revue Francaise de Photogrammetrie Et de Teledetection 209: 23–29.
  • Radford, A., L. Metz, and S. Chintala. 2015. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” arXiv preprint arXiv:1511.06434. doi:10.48550/arXiv.1511.06434.
  • Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, and N. Carvalhais. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1.
  • Saha, S., F. Bovolo, and L. Bruzzone. 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.2018.2886643.
  • Saito, K., R.J. Spence, C. Going, and M. Markus. 2004. “Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study Following the 26 January 2001 Gujarat Earthquake.” Earthquake Spectra 20 (1): 145–169. doi:10.1193/1.1650865.
  • Shao, Z., and C. Liu. 2014. “The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta.” Remote Sensing 6 (10): 9359–9378. doi:10.3390/rs6109359.
  • Shao, Z., and J. Cai. 2018. “Remote Sensing Image Fusion with Deep Convolutional Neural Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (5): 1656–1669. doi:10.1109/JSTARS.2018.2805923.
  • Shao, Z., H. Fu, D. Li, O. Altan, and T. Cheng. 2019a. “Remote Sensing Monitoring of Multi-Scale Watersheds Impermeability for Urban Hydrological Evaluation.” Remote Sensing of Environment 232: 111338. doi:10.1016/j.rse.2019.111338.
  • Shao, Z., Y. Pan, C. Diao, and J. Cai. 2019b. “Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 4062–4076. doi:10.1109/TGRS.2018.2889677.
  • Shi, W., M. Zhang, R. Zhang, S. Chen, and Z. Zhan. 2020. “Change Detection Based on Artificial Intelligence: State-Of-The-Art and Challenges.” Remote Sensing 12 (10): 1688. doi:10.3390/rs12101688.
  • Silván-Cárdenas, J.L., and L. Wang. 2014. “On Quantifying Post-Classification Subpixel Landcover Changes.” ISPRS Journal of Photogrammetry and Remote Sensing 98: 94–105. doi:10.1016/j.isprsjprs.2014.09.018.
  • Simard, P.Y., D. Steinkraus, and J.C. Platt. 2003. “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis.” Seventh International Conference on Document Analysis and Recognition, 958–963. doi:10.1109/ICDAR.2003.1227801.
  • Singh, A. 1989. “Review Article Digital Change Detection Techniques Using Remotely-Sensed Data.” International Journal of Remote Sensing 10 (6): 989–1003. doi:10.1080/01431168908903939.
  • Song, A., J. Choi, Y. Han, and Y. Kim. 2018. “Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks.” Remote Sensing 10 (11): 1827. doi:10.3390/rs10111827.
  • Su, L., J. Shi, P. Zhang, Z. Wang, and M. Gong. 2016. “Detecting Multiple Changes from Multi-Temporal Images by Using Stacked Denosing Autoencoder Based Change Vector Analysis.” In 2016 International Joint Conference on Neural Networks (IJCNN), 1269–1276. doi:10.1109/IJCNN.2016.7727343.
  • Su, L., M. Gong, P. Zhang, M. Zhang, J. Liu, and H. Yang. 2017. “Deep Learning and Mapping Based Ternary Change Detection for Information Unbalanced Images.” Pattern Recognition 66: 213–228. doi:10.1016/j.patcog.2017.01.002.
  • Su, L., and X. Cao. 2018. “Fuzzy Autoencoder for Multiple Change Detection in Remote Sensing Images.” Journal of Applied Remote Sensing 12 (3): 035014. doi:10.1117/1.jrs.12.035014.
  • Sublime, J., and E. Kalinicheva. 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/rs11091123.
  • Tang, Y., X. Huang, and L. Zhang. 2013. “Fault-Tolerant Building Change Detection from Urban High-Resolution Remote Sensing Imagery.” IEEE Geoscience and Remote Sensing Letters 10 (5): 1060–1064. doi:10.1109/LGRS.2012.2228626.
  • Tewkesbury, A.P., A.J. Comber, N.J. Tate, A. Lamb, and P.F. Fisher. 2015. “A Critical Synthesis of Remotely Sensed Optical Image Change Detection Techniques.” Remote Sensing of Environment 160: 1–14. doi:10.1016/j.rse.2015.01.006.
  • Volpi, M., G. Camps-Valls, and D. Tuia. 2015. “Spectral Alignment of Multi-Temporal Cross-Sensor Images with Automated Kernel Canonical Correlation Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 107: 50–63. doi:10.1016/j.isprsjprs.2015.02.005.
  • Waldeland, A.U., J.H. Reksten, and A.B. Salberg. 2018. “Avalanche Detection in Sar Images Using Deep Learning.” In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2386–2389. doi:10.1109/IGARSS.2018.8517536.
  • Wang, Q., X. Zhang, G. Chen, F. Dai, Y. Gong, and K. Zhu. 2018a. “Change Detection Based on Faster R-CNN for High-Resolution Remote Sensing Images.” Remote Sensing Letters 9 (10): 923–932. doi:10.1080/2150704x.2018.1492172.
  • Wang, S., D. Quan, X. Liang, M. Ning, Y. Guo, and L. Jiao. 2018b. “A Deep Learning Framework for Remote Sensing Image Registration.” ISPRS Journal of Photogrammetry and Remote Sensing 145: 148–164. doi:10.1016/j.isprsjprs.2017.12.012.
  • Wang, Q., Z. Yuan, Q. Du, and X. Li. 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.2018.2849692.
  • Weng, Q. 2002. “Land Use Change Analysis in the Zhujiang Delta of China Using Satellite Remote Sensing, GIS and Stochastic Modelling.” Journal of Environmental Management 64 (3): 273–284. doi:10.1006/jema.2001.0509.
  • Xiao, R., R. Cui, M. Lin, L. Chen, Y. Ni, and X. Lin. 2018. “SOMDNCD: Image Change Detection Based on Self-Organizing Maps and Deep Neural Networks.” IEEE Access 6: 35915–35925. doi:10.1109/ACCESS.2018.2849110.
  • Xu, Y., S. Xiang, C. Huo, and C. Pan. 2013. “Change Detection Based on Auto-Encoder Model for VHR Images.” Mippr 2013: Pattern Recognition and Computer Vision 8919: 891902. doi:10.1117/12.2031104.
  • Yosinski, J., J. Clune, Y. Bengio, and H. Lipson. 2014. “How Transferable are Features in Deep Neural Networks?” Advances in Neural Information Processing Systems. 3320–3328.
  • Yu, X., X. Wu, C. Luo, and P. Ren. 2017. “Deep Learning in Remote Sensing Scene Classification: A Data Augmentation Enhanced Convolutional Neural Network Framework.” GIScience & Remote Sensing 54 (5): 741–758. doi:10.1080/15481603.2017.1323377.
  • Yuan, F., K.E. Sawaya, B.C. Loeffelholz, and M.E. Bauer. 2005. “Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing.” Remote Sensing of Environment 98 (2–3): 317–328. doi:10.1016/j.rse.2005.08.006.
  • Yuan, Z., Q. Wang, and X. Li. 2018. “ROBUST Pcanet for Hyperspectral Image Change Detection.” In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 4931–4934. doi:10.1109/IGARSS.2018.8518196.
  • Yuan, Q., H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, et al. 2020. ”Deep Learning in Environmental Remote Sensing: Achievements and Challenges”. Remote Sensing of Environment 241: 111716. 10.1016/j.rse.2020.111716.
  • Zhan, Y., K. Fu, M. Yan, X. Sun, H. Wang, and X. Qiu. 2017. “Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images.” IEEE Geoscience and Remote Sensing Letters 14 (10): 1845–1849. doi:10.1109/LGRS.2017.2738149.
  • Zhan, T., M. Gong, J. Liu, and P. Zhang. 2018. “Iterative Feature Mapping Network for Detecting Multiple Changes in Multi-Source Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 146: 38–51. doi:10.1016/j.isprsjprs.2018.09.002.
  • Zhang, H., and P. Zhang. 2016. “Deep Difference Representation Learning for Multi-Spectral Imagery Change Detection.” Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science, 80: 1008–1014. doi:10.2991/icamcs-16.2016.204.
  • Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 22–40. doi:10.1109/MGRS.2016.2540798.
  • Zhang, H., M. Gong, P. Zhang, L. Su, and J. Shi. 2016a. “Feature-Level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery.” IEEE Geoscience and Remote Sensing Letters 13 (11): 1666–1670. doi:10.1109/Lgrs.2016.2601930.
  • Zhang, P., M. Gong, L. Su, J. Liu, and Z. Li. 2016b. “Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-Spatial-Resolution Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 116: 24–41. doi:10.1016/j.isprsjprs.2016.02.013.
  • Zhang, H., X. Ning, Z. Shao, and H. Wang. 2019. “Spatiotemporal Pattern Analysis of China’s Cities Based on High-Resolution Imagery from 2000 to 2015.” ISPRS International Journal of Geo-Information 8 (5): 241. doi:10.3390/ijgi8050241.
  • Zhang, R., Z. Shao, X. Huang, J. Wang, and D. Li. 2020. “Object Detection in UAV Images via Global Density Fused Convolutional Network.” Remote Sensing 12 (19): 3140. doi:10.3390/rs12193140.
  • Zhao, J., M. Gong, J. Liu, and L. Jiao. 2014. “Deep Learning to Classify Difference Image for Image Change Detection.” In 2014 International Joint Conference on Neural Networks (IJCNN), 411–417. doi:10.1109/IJCNN.2014.6889510.
  • Zhao, Q., J. Ma, M. Gong, H. Li, and T. Zhan. 2016. “Three-Class Change Detection in Synthetic Aperture Radar Images Based on Deep Belief Network.” Journal of Computational and Theoretical Nanoscience 13 (6): 3757–3762. doi:10.1166/jctn.2016.5208.
  • Zhao, W., Z. Wang, M. Gong, and J. Liu. 2017. “Discriminative Feature Learning for Unsupervised Change Detection in Heterogeneous Images Based on a Coupled Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 55 (12): 7066–7080. doi:10.1109/TGRS.2017.2739800.
  • Zhou, J., B. Yu, and J. Qin. 2014. “Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale.” Remote Sensing 6 (9): 9086–9103. doi:10.3390/rs6099086.
  • Zhou, L., Z. Shao, S. Wang, and X. Huang. 2022. “Deep Learning-Based Local Climate Zone Classification Using Sentinel-1 SAR and Sentinel-2 Multispectral Imagery.” Geo-Spatial Information Science 1–16. doi:10.1080/10095020.2022.2030654.
  • Zhu, X., D. Tuia, L. Mou, G. Xia, L. Zhang, F. Xu, and F. Fraundorfer. 2017. “Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 8–36. doi:10.1109/MGRS.2017.2762307.
  • Zhu, Z. 2017. “Change Detection Using Landsat Time Series: A Review of Frequencies, Preprocessing, Algorithms, and Applications.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 370–384. doi:10.1016/j.isprsjprs.2017.06.013.
  • Zhu, B., H. Gao, X. Wang, M. Xu, and X. Zhu. 2018. “Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology.” In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 55–59. doi:10.1109/ICIVC.2018.8492747.
  • Zhu, H., M. Ma, W. Ma, L. Jiao, S. Hong, J. Shen, and B. Hou. 2021. “A Spatial-Channel Progressive Fusion ResNet for Remote Sensing Classification.” Information Fusion 70: 72–87. doi:10.1016/j.inffus.2020.12.008.

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