Abstract
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.
Highlights
Space-borne imaging is a standard way of acquiring information about the objects on the Earth surface
The assessments of the proposed approach are performed over 10 test images which differ from their urban area and building characteristics as well as from their illumination and acquisition conditions
Vegetation and shadow areas are extracted with the help of the multi-spectral information widely accessible to the most of the high resolution satellite images
Summary
Space-borne imaging is a standard way of acquiring information about the objects on the Earth surface. Satellite images are one of the most important data input source to be utilized for the purpose of object detection. The detection of man-made features from satellite images is of great practical interest for a number of applications such as urban monitoring, change detection, estimation of human population etc. In an early work, Huertas and Nevatia (1988) emphasized the importance of the automation for the detection, and they stated the major task: the extraction and description of man-made objects, such as buildings. Up to now from their early paper, various researchers belonging to different scientific communities involved for the same task, and a significant number of research studies have been published. Since this paper is devoted to the automated detection of buildings from a single optical image, we very briefly summarize the previous studies aimed to automatically detect buildings from monocular optical images
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