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

Zhang, Xinzheng; Su, Hang; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584; 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. 79-94. 10.1016/j.isprsjprs.2021.01.004

Before downloading, please read NORA policies.

Preview

Text N529686PP.pdf - Accepted Version Download (4MB) | Preview

Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.isprsjprs.2021.01.004
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	0924-2716
Additional Keywords:	change detection, synthetic aperture radar, difference image, fuzzy c-means algorithm, deep learning
NORA Subject Terms:	Electronics, Engineering and Technology Data and Information
Date made live:	19 Feb 2021 12:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529686

Actions (login required)

View Item

Document Downloads

More statistics for this item...