Change Detection in SAR Images Based on Deep Learning

Hatem Magdy Keshk1,2, Xu-Cheng Yin2
1National Authority for Remote Sensing and Space Science, Cairo, Egypt
2University of Science and Technology Beijing, Beijing, China

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Tài liệu tham khảo

Chen Y, Zhang R, Yin D (2012) Multi-polarimetric SAR image compression based on sparse representation. Sci China 55(8):1888–1897

Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151

Lunetta RS, Elvidge CD (1999) Remote sensing change detection: environmental monitoring methods and applications. Ann Arbor Press, Chelsea, MI, Taylor & Francis Ltd, UK, pp xviii + p 318

Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD (2006) Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ 105(2):142–154

Manonmani R, Mary Divya Suganya G (2010) Remote sensing and GIS application in change detection study in urban zone using multi temporal satellite. Int J Geomat Geosci 4(2):339–348

Yousif O, Ban Y (2014) Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights. IEEE J Sel Top Appl Earth Observ Remote Sens 7(10):4288–4300

Zhang J, Xie L, Tao X (2002) Change detection of remote sensing image for earthquake damaged buildings and its application in seismic disaster assessment. J Nat Disasters 11(2):59–64 (in Chinese)

Zhang J-F, Xie L-L, Tao X-X (2003) Change detection of earthquake-damaged buildings on remote sensing image and its application in seismic disaster assessment. In: IGARSS 2003. 2003 IEEE international geoscience and remote sensing symposium. Proceedings (IEEE Cat No 03CH37477), Toulouse, vol.4. pp 2436–2438. https://doi.org/10.1109/IGARSS.2003.1294467

Hame T, Heiler I, Miguel-Ayanz JS (1998) An unsupervised change detection and recognition system for forestry. Int J Remote Sens 19(6):1079–1099. https://doi.org/10.1080/014311698215612

Jiao L-C, Zhao J, Yang S-Y, Liu F (2017) Deep learning, optimization and recognition. Tsinghua University Press (TUP), Beijing (in Chinese)

Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nat J 521(7553):436. https://doi.org/10.1038/nature14539

Deng L, Li J, Huang J-T, Yao K, Yu D, Seide F, Seltzer M, Zweig, G, He X, Williams J, Gong Y, Acero A (2013) Recent advances in deep learning for speech research at Microsoft. In: International conference on acoustics, speech, and signal processing, USA. ICASSP-88, pp 8604–8608. https://doi.org/10.1109/ICASSP.2013.6639345

Keshk H, Yin X-C (2019) Classification of EgyptSat-1 images using deep learning methods. Int J Sens Wirel Commun Control 9:1. https://doi.org/10.2174/2210327909666190207153858

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Proceedings of the 25th international conference on neural information processing systems (NIPS'12), vol. 1. Curran Associates Inc., USA, pp 1097–1105

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

Arel I et al (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5:13–18

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

Lange S, Riedmiller M (2010) Deep auto-encoder neural networks in reinforcement learning. In: Proceedings of the international joint conference on neural networks (IJCNN), Barcelona, pp 1–8

Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3:2672–2680

Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307. https://doi.org/10.1109/TIP.2004.838698

Inglada J, Mercier G (2007) A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis. IEEE Trans Geosci Remote Sens 45(5):1432–1445. https://doi.org/10.1109/TGRS.2007.893568

Liao P-S, Chen T-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727

Lee J-S, Pottier E (2009) Polarimetric radar imaging: from basics to applications. Int JRemote Sens 33(1):333–334. https://doi.org/10.1080/01431161.2010.519925

Gong M, Jia M, Su L, Wang S, Jiao L (2014) Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts. Int J Remote Sens 35(11–12):4009–4030. https://doi.org/10.1080/01431161.2014.916054

Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv:1301.3557

Nupur Saxena NR (2013) A review on speckle noise filtering techniques for SAR images. Int J Adv Res Comput Sci Electron Eng 2(2):243–247

Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46. https://doi.org/10.1177/001316446002000104

Su L, Gong M, Sun B, Jiao L (2014) Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm. Int J Remote Sens 35(2):621–650. https://doi.org/10.1080/01431161.2013.871596

Moser G, Serpico SB (2006) Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery. IEEE Trans Geosci Remote Sens 44(10):2972–2982. https://doi.org/10.1109/TGRS.2006.876288