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Superpixel Based Unsupervised Change Detection of Manmade Targets on Satellite Images

In this paper, a novel solution to the problem of change detection in bitemporal satellite images is presented. The approach can be described as 1) Preprocessing, in which both images' luminance levels are approximated to the same distribution via the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and noise is filtered out by the use of a Bilateral Noise Reduction (BNR) filter; 2) Extraction of a joint superpixel map of both images using a novel method; 3) Calculation of a variety of metrics at the superpixel level that are directed towards finding man-made changes; 4) Calculating a final change mask from a final difference image via thresholding. The proposed method is tested on bitemporal images of a number of urban regions.