A Hierarchical Building Detection Method for Very High Resolution Remotely Sensed Images Combined with DSM Using Graph Cut Optimization

Detecting buildings in remotely sensed data plays an important role for urban analysis and geographical information systems. This study proposes a hierarchical approach for extracting buildings from very high resolution (9 cm GSD (Ground Sampling Distance)), multi-spectral aerial images and matched DSMs (Digital Surface Models). There are three steps in the proposed method: first, shadows are detected with a morphological index, and corrected for Amyl (Normalized Difference Vegetation Index) computation; second, the NDVI is incorporated using a top-hat reconstruction of the DSM to obtain the initial building mask; finally, a graph cut optimization based on modified superpixel segmentation is carried out to consolidate building segments with high probability and thus eliminates segments that have low probability to be buildings. Experiments were performed over the whole Vaihingen dataset, covering 3.4 km(2) with around 3000 buildings. The proposed algorithm effectively extracted 94 percent of the buildings with 87 percent correctness. This demonstrates that the proposed method achieved satisfactory results over a large dataset and has the potential for many practical applications.