Robust Detection of Buildings from a Single Color Aerial Image

In this study, a robust methodology for the detection of buildings from a single color aerial image is proposed. The methodology is initialized with the mean-shift segmentation algorithm. Next, a vector-valued canny edge detection algorithm which relies on the photometric quasi-invariant gradients is utilized to detect edges from the color segmented image. Morphological operations are applied to the edge image and two raster datasets are generated from the morphologically reconstructed edge image, (i) the edge pixels that form closed-boundary shapes, and (ii) the edge pixels that do not form closed-boundary shapes. The first dataset, the edge pixels that form closed-boundary shapes, are vectorized using boundary tracing followed with the douglas-peucker simplification algorithm. On the other hand, a minimum bounding convex-hull algorithm followed with gradient vector flow (GVF) snake is used to generate polygons from the second dataset. The polygon results of both datasets are joined together in a unification step. In a final verification stage, two vegetation indices are used to mask out the polygons that belong to healthy vegetated areas. One color (RGB) aerial ortho-image with a resolution of 30 cm is utilized to test the performance of the proposed methodology. Based on the results computed, the edge detector suppressed the shadows and highlight edges with accuracy around 99%. Among the available 251 apartment buildings in the site, 231 of them were almost completely detected. The algorithm provided %73 accuracy for the buildings that are in a neighboring house condition (810 out of 1104 are detected).