SAR image change detection via generalized extreme value (GEV) modeling

The rapid growth of high-resolution synthetic aperture radar (SAR) images has created new challenges for change detection methods. High-resolution SAR images often exhibit extremely heterogeneous terrain, resulting in severe long-tailed distributions in the image histogram. Traditional change detection methods based on hypothesis testing theory rely on Gamma distributions, which struggle to accurately model the complex scenes in high-resolution images. Recently the Generalized Extreme Value (GEV) distribution is proven effective in describing the long-tail phenomenon. In this paper, we introduce GEV model into the hypothesis test theory and propose a GEV-based SAR change detection method. First, there may exist two or more heterogeneous components in a certain scene in high-resolution SAR images. We oversegment the image into homogeneous local regions using a superpixel algorithm and model each region with the GEV distribution. Based on this distribution, we then derive the GEV-based likelihood-ratio test (LRT) statistics to measure the similarity of two superpixels for unsupervised change detection. Finally, by analyzing the asymptotic behavior of the GEV-based LRT, we apply a threshold to obtain the change maps (CMs). To evaluate the performance of our approach, we conduct Monte Carlo experiments using empirical data to investigate the goodness-of-fit performance and asymptotic behavior of the LRT. Our method demonstrates superior performance compared to state-of-the-art approaches, achieving the highest overall accuracy in both studied areas.