Superpixel-Based 3D Building Model Refinement and Change Detection, Using VHR Stereo Satellite Imagery

摘要

Buildings are one of the main objects in urban remote sensing and photogrammetric computer vision applications using satellite data. In this paper a superpixel-based approach is presented to refine 3D building models from stereo satellite imagery. First, for each epoch in time, a multispectral very high resolution (VHR) satellite image is segmented using an efficient superpixel, called edge-based simple linear iterative clustering (ESLIC). The ESLIC algorithm segments the image utilizing the spectral and spatial information, as well as the statistical measures from the gray-level co-occurrence matrix (GLCM), simultaneously. Then the resulting superpixels are imposed on the corresponding 3D model of the scenes taken from each epoch. Since ESLIC has high capability of preserving edges in the image, normalized digital surface models (nDSMs) can be modified by averaging height values inside superpixels. These new normalized models for epoch 1 and epoch 2, are then used to detect the 3D change of each building in the scene.