Comparing the Performance of Mathematical Morphology and Bhattacharyya Distance for Airport Extraction

Abstract

Remote Sensing has been of paramount importance to capture features of interest from the Earth's surface. In this context, extraction algorithms and classification methods can be applied to capture the response of the electromagnetic spectrum in different image bands in order to find out what kind of feature is more predominant in a satellite image. Therefore, in this paper, two different feature detection approaches are evaluated and compared: the first one based on mathematical morphology filtering, while the second one is built as a semi-supervised classification approach which applies the well-established Bhattacharyya distance. Mathematical Morphology is an important field of Digital Image Processing which aims at detecting and extracting image objects based on the set theory and convolution processes. Bhattacharyya distance is one of the most effective statistical tools for classifying image zones. In our experiments, both approaches are compared against each other by inspecting their classification results for two airport areas, which includes both visual as well as quantitative evaluations.