Change Detection in SAR Images Based on Deep Learning

  • Hatem Magdy KeshkEmail author
  • Xu-Cheng Yin
Original Paper


Change detection in remote-sensing images is used to detect changes during different time periods on the surface of the Earth. Because of the advantages of synthetic aperture radar (SAR), which is not affected by time, weather or other conditions, change-detection technology based on SAR images has important research value. At present, this technology has attracted the attention of increasingly more researchers, and has also been used extensively in diverse fields, such as urban planning, disaster assessment, and forest early warning systems. Our objective in this paper is to combine both the change detection of SAR images with the deep neural networks to compare its efficiency with fuzzy clustering method and deep belief network. Our experiments, conducted on real data sets and theoretical analysis, indicates the advantages of the proposed method. Our results appear that proposed deep-learning algorithms can further improve the change-detection process.


Change detection SAR Remote sensing Deep learning 



This study was partially supported by University of Science and Technology Beijing.

Author contributions

HMK conceived, designed, and performed the experiments; HMK and X-CY analyzed the data; HMK wrote the paper.

Compliance with Ethical Standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Chen Y, Zhang R, Yin D (2012) Multi-polarimetric SAR image compression based on sparse representation. Sci China 55(8):1888–1897MathSciNetzbMATHGoogle Scholar
  2. 2.
    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):2141–2151MathSciNetCrossRefGoogle Scholar
  3. 3.
    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 318Google Scholar
  4. 4.
    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):142–154CrossRefGoogle Scholar
  5. 5.
    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):339–348Google Scholar
  6. 6.
    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):4288–4300CrossRefGoogle Scholar
  7. 7.
    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) Google Scholar
  8. 8.
    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.
  9. 9.
    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. CrossRefGoogle Scholar
  10. 10.
    Jiao L-C, Zhao J, Yang S-Y, Liu F (2017) Deep learning, optimization and recognition. Tsinghua University Press (TUP), Beijing (in Chinese) Google Scholar
  11. 11.
    Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nat J 521(7553):436. CrossRefGoogle Scholar
  12. 12.
    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.
  13. 13.
    Keshk H, Yin X-C (2019) Classification of EgyptSat-1 images using deep learning methods. Int J Sens Wirel Commun Control 9:1. CrossRefGoogle Scholar
  14. 14.
    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 1097–1105Google Scholar
  15. 15.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  16. 16.
    Arel I et al (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5:13–18CrossRefGoogle Scholar
  17. 17.
    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828CrossRefGoogle Scholar
  18. 18.
    Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    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 1–8Google Scholar
  20. 20.
    Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3:2672–2680Google Scholar
  21. 21.
    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. MathSciNetCrossRefGoogle Scholar
  22. 22.
    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. CrossRefGoogle Scholar
  23. 23.
    Liao P-S, Chen T-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727Google Scholar
  24. 24.
    Lee J-S, Pottier E (2009) Polarimetric radar imaging: from basics to applications. Int JRemote Sens 33(1):333–334. CrossRefGoogle Scholar
  25. 25.
    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. CrossRefGoogle Scholar
  26. 26.
    Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv:1301.3557
  27. 27.
    Nupur Saxena NR (2013) A review on speckle noise filtering techniques for SAR images. Int J Adv Res Comput Sci Electron Eng 2(2):243–247Google Scholar
  28. 28.
    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46. CrossRefGoogle Scholar
  29. 29.
    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. CrossRefGoogle Scholar
  30. 30.
    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. CrossRefGoogle Scholar

Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.National Authority for Remote Sensing and Space ScienceCairoEgypt

Personalised recommendations