New applications for mathematical morphology in urban feature extraction from high-resolution satellite imagery

Abstract

Recently available commercial high-resolution satellite imaging sensors provide an important source for urban remote sensing applications. The high spatial image resolution reveals very fine details in urban areas and greatly facilitates the extraction of urban-related features such as roads, buildings, and vehicles. Since many urban land cover types have significant spectral overlap, structural information obtained using mathematical morphologic operators can provide complementary information to improve discrimination of different urban features. Here we present research demonstrating new applications of mathematical morphology for urban feature extraction from high-resolution satellite imagery. For image preprocessing, an alternating sequential filter is used to eliminate small spatial-scale disturbances to facilitate the extraction of larger-scale structures. For road extraction, directional morphological filtering is exploited to mask out those structures shorter than the distance of a typical city block. For building extraction, a recently introduced concept called the differential morphological profile (DMP) is used to generate building and shadow hypotheses. For vehicle detection, a morphological shared-weight neural network is used to classify image pixels on roads into target and non-target. Thus, mathematical morphology has a wide variety of useful applications for urban feature extraction from high-resolution satellite imagery.