Robust unsupervised small area change detection from SAR imagery using deep learning

Xinzheng Zhang*, Hang Su, Ce Zhang, Xiaowei Gu, Xiaoheng Tan, Peter M. Atkinson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.

Original languageEnglish
Pages (from-to)79-94
Number of pages16
JournalISPRS Journal of Photogrammetry and Remote Sensing
Early online date17 Jan 2021
Publication statusPublished - 01 Mar 2021


  • Change detection
  • Deep learning
  • Difference image
  • Fuzzy c-means algorithm
  • Synthetic aperture radar


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