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This article proposes the Morphological Slum Index (MSI) approach for extracting slum areas from Very High Resolution (VHR) satellite images. An MSI is built by using different morphological operators. Since, the non-slum objects (buildings, vegetation, roads and open areas) are brighter than their neighbourhoods and show similar spectral properties as those of slums, MSI classifies the non-slum objects as slum objects. To optimize the misclassification of MSI, a post-processing technique called Morphological Spatial Pattern Analysis (MSPA) is used. Three different VHR images acquired by the WorldView-2 sensor (1.84 m resolution) of Madurai city, India and one image acquired by the WorldView-3 Sensor (0.31 m resolution) of Kibera, Kenya are used as the test images to investigate the qualitative and quantitative results of the proposed technique. From the classified outputs, the proposed MSI with MSPA approach attains an overall accuracy of 95.78%, 96.42%, 95.12% and 92.64% for test images 1, 2, 3 and 4, respectively.

Morphological slum index for slum extraction from high-resolution remote sensing imagery over urban areas