Urban area change detection based on generalized likelihood ratio test

Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.