Morphological House Extraction from Digital Surface Model and Imagery of High-Density Residential Areas

High-density residential area modeling is extremely difficult because many houses reside close to or even touch each other; it usually causes the error of under segmentation. Our method solves this issue through scale detection and dome reshaping. A modified granulometry using opening-by-reconstruction instead of opening is proposed to detect the principle scales of the buildings. A morphological filtering algorithm at the detected continuous scales is developed to decompose a house into reshaped slices, which will be reconstructed as a dome. Thus, the domes are separate despite the joined houses, and are used to extract the markers of the houses. Finally, processes based on markers are developed to segment and model the houses. Among which, the multiple spectral image is used to detect the trees in the scene. Compared with other extraction methods, our technique decreases the fragment rate from 7.4 percent to 0.9 percent, the mixing rate from 14.8 percent to 0.9 percent.