Finding edges of buildings via a junction process in high-resolution remotely sensed images

This paper addresses the problem of finding edges of buildings in high-resolution remotely sensed images, which is of great help for subsequent analysis of built-up areas in urban remote sensing. More precisely, we propose a novel algorithm to extract meaningful edges associated to buildings with their saliency, by integrating an edge detection procedure with a junction process. This is inspired by the observation that meaningful junctions mainly emerge around buildings rather than on non-building objects in high-resolution remote sensing images. Thus, given a high-resolution remotely sensed image, we first use an edge detection algorithm, e.g. canny edge extractor, to compute all possible candidate edges of buildings, and then refine those candidates by meaningful junctions around buildings with their significance. The meaningful junctions are provided by an a-contrario junction detector, whose significance is related to the structural saliency in the images. For the evaluation of the proposed method, we test it on a small set of remote sensing images of half-meter resolution from WorldView-2, IKONOS and QuicBird. It demonstrates that our approach can find and locate edges of buildings with high precision and efficiency.