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Change Detection Approach for SAR Imagery Based on Arc-Tangential Difference Image and k-Means++
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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… Click to show full abstract
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.
