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How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

Rongfang Wang,Jiawei Chen,L. Jiao,Mi Wang

Published 2019 in Remote Sensing

ABSTRACT

Change detection on bitemporal synthetic aperture radar (SAR) images is a key branch of SAR image interpretation. However, it is challenging due to speckle and unavoidable registration errors within bitemporal SAR images. A key issue is whether and how despeckling and structural features can improve accuracy. In this paper, we investigate how despeckling and structural features can benefit change detection for SAR images. Several change detection methods were performed on both input images and the corresponding despeckled images, where despeckling was achieved by different methods. The comparisons demonstrate that despeckling methods that preserve the structures can suppress noise in difference images and can improve the accuracy of change detection. We also developed a sparse model to exploit structural features from the difference images while reducing the influence of misalignment between bitemporal SAR images. The comparisons were performed on five datasets of bitemporal SAR images, and the experimental results show that our proposed sparse model outperforms other traditional methods, demonstrating the advantages of change detection.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Remote Sensing

  • Publication date

    2019-02-18

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.3390/RS11040421
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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