A Morphological Building Detection Framework for High-Resolution Optical Imagery Over Urban Areas

Abstract

This letter proposes an efficient framework for building detection from coarse to fine using morphological technique for high-resolution optical satellite imagery over urban areas. First, the preliminary result of building regions is obtained by the recently developed morphological building index (MBI) method, which is able to detect potential building structures. However, the raw results derived from the MBI can be subject to a number of false alarms, which are caused by bright soil, roads, and open areas. In this letter, we propose to use morphological spatial pattern analysis as a postprocessing to further optimize the MBI result and remove the commission errors. The original MBI result is then separated into seven mutually exclusive categories-core, islet, loop, bridge, perforation, edge, and branch-by applying a series of morphological transformations such as erosions, geodesic dilation, reconstruction by dilation, anchored skeletonization, etc. The objects corresponding to the generic categories are then analyzed, and the categories corresponding to building parts are maintained, while the others are abandoned. After this postprocessing, the small noisy patches and narrow roads, which were wrongly extracted by the MBI, can be removed. In addition, the shape of the buildings can also be regularized by removing the branches, and the holes contained in the building objects can be identified and filled. Extensive experiments performed on GeoEye-1 and WorldView-2 images confirm the effectiveness and robustness of the proposed morphological building detection framework.