Superpixel-Based Urban Change Detection in SAR Images Using Optimal Transport Distance

This paper presents a novel method to detect urban changes in synthetic aperture radar (SAR) images by using optimal transport distance (OTD) as the measurement of dissimilarity. First, the multichannel logarithm with Gaussian denoising (MuLoG) scheme is used to efficiently suppress the speckles of the SAR images. The despeckled images are then segmented into superpixels by the simple linear iterative clustering algorithm (SLIC). After that, the difference map is obtained via the use of Gaussian mixture model and OTD. Finally, change detection results are obtained by the generalized Kitler and Illingworth (GKI) thresholding algorithm. Experimental results on TerraSAR-X images over urban areas show the effectiveness of the proposed method.