Using Structural Features to Detect Buildings in Panchromatic Satellite Images

Abstract

Detecting buildings from very high resolution aerial and satellite images is very important for mapping, urban planning, and land use analysis. Although it is possible to manually locate buildings from these very high resolution images; this operation may not be robust and fast. Therefore, automated systems to detect buildings from very high resolution aerial and satellite images are needed. Unfortunately, solution is not straightforward due to diverse characteristics and uncontrolled imaging conditions of the scenes. To overcome these difficulties, herein we propose a novel solution to detect buildings from very high resolution grayscale aerial and panchromatic Ikonos satellite images using structural features and probability theory. For this purpose, we extract structural features from given test image using a steerable filter set. Extracted structural features indicate geometrical properties of objects in the image. Using them, we estimate probability density function (pdf) which indicates locations of buildings to be detected. Our extensive tests on a large and diverse data set including images taken from different scenes and from different sensors indicate high robustness and practical usefulness of the algorithm.