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 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, the solution is not straightforward due to the 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 the given test image using a steerable filter set. Extracted structural features indicate geometrical properties of objects in the image. Using them, we estimate the probability density function (pdf) which indicates locations of buildings to be detected. Our extensive tests on a large and diverse data set indicate the robustness and practical usefulness of our method.
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