2D/3D information fusion for building extraction from high-resolution satellite stereo images using kernel graph cuts

Abstract

This study presents a building extraction strategy from high resolution satellite stereo images (HRSSI) using 2D and 3D information fusion. In the 2D processing strategy, a visible Vegetation index (VVI) is generated. In the 3D processing, a disparity map is generated using semi-global matching (SGM). To remove defects from the disparity map, an object-based approach is proposed by using mean-shift image segmentation and extracting rectangles. By removing terrain effects, a normalized disparity map (nDM) is produced. In the next step, vegetation pixels are removed from nDM and an initial building mask is generated. As nDM does not have precise building boundaries, hybrid segmentation by the kernel graph cut (KGC) is applied to the feature space including the RGB, nDM, and VVI and the results are used in a decision Level fusion step. By this methodology, segments that are highly intersected with initial building mask are classified as buildings. Finally, a building boundary refinement (BBR) algorithm is applied to buildings for removing the remaining defects. The proposed method is applied to two pairs of GeoEye-1 stereo images including residential and industrial test areas. Evaluation results show the completeness and correctness level of higher than 90% for the two test areas. Further evaluations show that the quality metric has significantly changed after decision level fusion using the KGC.