Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform

The morphological hit-or-miss transform (HMT) was found to be efficient for the detection of buildings in panchromatic bands of very high spatial resolution images. The use of multispectral information was judged to be necessary to improve the results. The application of morphological operators to multispectral images is problematic, as no universal strategy for ordering the multivalued pixels of these images has been widely adopted. In this paper, we propose a new method to detect building locations based on a recently developed concept for the HMT to handle noise, called percentage occupancy HMT (POHMT). The parameters for the POHMT were defined with the aid of the top-hat by reconstruction transformation. To eliminate irrelevant locations, we applied a vegetation mask and verified locations by their proximity to shadows. The novelty of the method consists in the proposed vector-based strategy that allows for the application of the POHMT to multispectral images in order to detect building locations. Moreover, an original technique to automatically define the parameters for the POHMT was proposed. The method was tested on subsets from a pan-sharpened Ikonos image and from raw GeoEye and WorldView-2 images. The experimental results are promising.