Unsupervised Detection of Built-Up Areas From Multiple High-Resolution Remote Sensing Images

Given a set of high-resolution remote sensing images covering different scenes, we propose an unsupervised approach to simultaneously detect possible built-up areas from them. The motivation behind is that the frequently recurring appearance patterns or repeated textures corresponding to common objects of interest (e.g., built-up areas) in the input image data set can help us discriminate built-up areas from others. With this inspiration, our method consists of two steps. First, we extract a large set of corners from each input image by an improved Harris corner detector. Afterward, we incorporate the extracted corners into a likelihood function to locate candidate regions in each input image. Given a set of candidate build-up regions, in the second stage, we formulate the problem of build-up area detection as an unsupervised grouping problem. The candidate regions are modeled through texture histogram, and the grouping problem is solved by spectrum clustering and graph cuts. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.