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

Zhang, Xinzheng and Su, Hang and Zhang, Ce and Gu, Xiaowei and Tan, Xiaoheng and Atkinson, Peter (2021) Robust unsupervised small area change detection from SAR imagery using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 173. pp. 79-94. ISSN 0924-2716

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
ISPRS Journal of Photogrammetry and Remote Sensing
Additional Information:
This is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 173, 2021 DOI: 10.1016/j.isprsjprs.2021.01.004
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3305
Subjects:
ID Code:
150399
Deposited By:
Deposited On:
06 Jan 2021 10:50
Refereed?:
Yes
Published?:
Published
Last Modified:
13 Jul 2021 04:41