A Region-Based Approach to Building Detection in Densely Build-Up High Resolution Satellite Image

We propose a novel region-based approach for building detection in high-resolution satellite image with densely build-up buildings. In our method, first the prior building model is constructed with texture and shape features from training building set. After over-segmentation of input image into many small regions, we identify regions which have a similar pattern with prior building model. These regions are called building like regions (BLRs). Then we group BLRs to get candidate building regions (CBRs), which have similar shape with prior building model. Next, lines which have strong relationship with each CBR are extracted. From these lines and CBR boundaries, 2-D rooftop hypotheses are generated. At last, shadows and geometrical rules are used to verify the hypotheses. Experimental results are shown on area with hundreds of buildings.