Exploiting Shadow Evidence and Iterative Graph-Cuts for Efficient Detection of Buildings in Complex Environments

Abstract. This paper presents an automated approach for efficient detection of building regions in complex environments. We investigate the shadow evidence to focus on building regions, and the shadow areas are detected by recently developed false colour shadow detector. The directional spatial relationship between buildings and their shadows in image space is modelled with the prior knowledge of illumination direction. To do that, an approach based on fuzzy landscapes is presented. Once all landscapes are collected, a pruning process is applied to eliminate the landscapes that may occur due to non-building objects. Thereafter, we benefit from a graph-theoretic approach to accurately detect building regions. We consider the building detection task as a binary partitioning problem where a building region has to be accurately separated from its background. To solve the two-class partitioning, an iterative binary graph-cut optimization is performed. In this paper, we redesign the input requirements of the iterative partitioning from the previously detected landscape regions, so that the approach gains an efficient fully automated behaviour for the detection of buildings. Experiments performed on 10 test images selected from QuickBird (0.6 m) and Geoeye-1 (0.5 m) high resolution datasets showed that the presented approach accurately localizes and detects buildings with arbitrary shapes and sizes in complex environments. The tests also reveal that even under challenging environmental and illumination conditions (e.g. low solar elevation angles, snow cover) reasonable building detection performances could be achieved by the proposed approach.