Enhanced change detection performance based on deep despeckling of synthetic aperture radar images

Ihmeida, M. ORCID: https://orcid.org/0009-0009-8728-6868 and Shahzad, M. ORCID: https://orcid.org/0009-0002-9394-343X (2023) Enhanced change detection performance based on deep despeckling of synthetic aperture radar images. IEEE Access. p. 1. ISSN 2169-3536 doi: 10.1109/access.2023.3307208

Abstract/Summary

Synthetic aperture radar (SAR) image change detection (CD) focuses on identifying changes between two images at different times for the same geographical region. Recently, several deep learning methods have been proposed for performing SAR based CD. However, speckle noise remains a major challenge for these methods. To address this, we propose a despeckling model (DM) that effectively suppresses speckle noise and enhances the performance of the existing CD methods. The proposed despeckling architecture is not only resilient to multi-temporal SAR acquired from one SAR imaging process (i.e., the same number of SAR images looks before and after the change) but also deals with any combination of single or multi-look images acquired prior and after the change. Moreover, as a second contribution, we have also proposed a loss function that effectively suppresses speckle noise, thereby improving the change detection accuracy. Both the despeckling model and the proposed tolerant noise loss function are evaluated extensively on three public real SAR datasets, achieving superior performance compared to existing state-of-the-art SAR CD methods in all datasets.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/113091
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
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