A perception-inspired building index for automatic built-up area detection in high-resolution satellite images

This paper addresses the problem of automatic extraction of built-up areas from high-resolution remote sensing images. We propose a new building presence index from the point view of perception. We argue that built-up areas usually result in significant corners and junctions in high-resolution satellite images, due to the man-made structures and occlusion, and thus can be measured by the geometrical structures they contained. More precisely, we first detect corners and junctions by relying on a perception-inspired corner detector, called an a-contrario junction detector. Each detected corner is associated with a perceptual significance, which measures the structural saliency of the corner in the image and is independent of the contrast and scale. All these detected corners together with their significance are then used to compute the building index. The proposed approach is evaluated on a high-resolution satellite image set, including 15 big images from GeoEye-1, QuickBird and IKONOS. The results demonstrated that our method achieves the state-of-the-art results and can be used in practical applications.