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

Zhang, Xinzheng, Su, Hang, Zhang, Ce, Gu, Xiaowei, Tan, Xiaoheng, Atkinson, Peter M. (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. (doi:10.1016/j.isprsjprs.2021.01.004) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90408)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.isprsjprs.2021.01.004

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: Article
DOI/Identification number: 10.1016/j.isprsjprs.2021.01.004
Uncontrolled keywords: Change detection; Synthetic aperture radar; Difference image; Fuzzy c-means algorithm; Deep learning
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 10:37 UTC
Last Modified: 29 Sep 2021 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90408 (The current URI for this page, for reference purposes)
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