Change Detection Approach for SAR Imagery Based on Arc-Tangential Difference Image and k-Means++

ATASEVER Ü. H., Gunen M. A.

IEEE Geoscience and Remote Sensing Letters, vol.19, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2022
  • Doi Number: 10.1109/lgrs.2021.3127964
  • Journal Name: IEEE Geoscience and Remote Sensing Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Arc-tangential difference, change detection (CD), k-Means plus, synthetic aperture radar (SAR), UNSUPERVISED CHANGE DETECTION, LOG-RATIO IMAGE, MODEL
  • Kayseri University Affiliated: No

Abstract

© 2004-2012 IEEE.In this letter, an unsupervised change detection (CD) approach based on arc-tangential difference and k -Means++ clustering is presented for synthetic aperture radar (SAR) remote-sensing images. The images are first standardized with their variance values using a logarithmic function applied to multitemporal images. The difference image (DI) is then calculated by subtracting the SAR images using the arc-tangential subtraction operator. After that, the DI is subjected to a 2-D Gaussian filter and a median filter, respectively. Filters are essential for determining the best feature space for CD. The 2-D Gaussian filter smooths DIs to retain local area consistency, while the median filter handles edge information. Finally, using k -Means++, a quick and efficient clustering approach, filtered data is clustered into two classes. Experiments using real-world datasets in Bern, Ottawa, and Yellow River have demonstrated that the given technique is fast, successful, and effective.