Automated Extraction of Slum Built-up Areas from Multispectral Imageries

Abstract

Slum areas are dense urban areas in which the building size is quite small, and the buildings are interconnected with each other. Also, there is a lot of variation in the texture of slum area buildings, which makes the extraction of individual buildings in slum areas a tough task. In this paper, a methodology has been proposed which aims to extract slum built-up areas using multispectral satellite images using MATLAB software. In the proposed methodology, two building masks have been prepared from the input image by using threshold value and Laws’ texture energy measure. After that, another building mask has been prepared by using these two masks and vegetation, non-building areas and shadow areas have been removed from it, which finally results in the detection of slum built-up areas. The proposed methodology has been applied on three subsets of QuickBird satellite image containing slum built-up areas. For accuracy assessment, the total slum area extracted from the proposed methodology has been compared with the total area obtained by manually digitized buildings. The overall accuracy of slum built-up extraction with respect to area has been found to be more than 83%. Due to resemblance of building and road texture, some over-extraction of road as slum built-up areas has also been observed in only one subset image. No over-detection has been found in other two subset images.