Abstract—The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.
This paper presents an automatic building detection technique using LIDAR data and multispectral imagery. Two masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect urban residential buildings, when assessed in terms of 15 indices including completeness, correctness and quality.