An enhanced approach for informal settlement extraction from optical data using morphological profile-guided filters: A case study of madurai city

Abstract

ABSTRACT Informal settlements are now becoming an unavoidable and growing phenomenon in the global south, whose locations are inappropriate in official statistics and maps. The distinctive spectral signature of informal settlements in high-resolution optical images causes difficulties in finding automatic approaches. This paper investigates the Multi-Shape Multi-Size Morphological Profile-Guided filter (MShMSiMP-GF) approach for detecting the informal settlements from high-resolution optical images. The development of the proposed approach was inspired by the Superpixel-based Guided Filter (SGF) approach, which uses superpixels to construct the guidance image. Though the superpixel based guidance image extracts detailed contextual information (scale, size), it fails to model geometrical information (shapes, structures) in an image. In order to incorporate the geometrical information, the MShMSiMP-GF approach generates multi-shape multi-size morphological profiles with increasing radius of guided filters. Quantitative and qualitative results of the proposed approach are investigated by four different images of Madurai city, India acquired by Kompsat-2 and WorldView-2 sensors. From the classified maps, it is observed that the proposed MShMSiMP-GF approach achieves an overall accuracy of 91.37%, 90.19%, 93.46% and 99.36% for the subsets 1, 2, 3 and 4, respectively.