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Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry\udcommunity. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban\udareas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for\udshadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is\uddeveloped to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and\udmultispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are\udemployed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant\udincrease in the rate of matched pixels compared to standard SGM algorithm

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

THE EFFECT OF SHADOW AREA ON SGM ALGORITHM AND DISPARITY MAP REFINEMENT FROM HIGH RESOLUTION SATELLITE STEREO IMAGES