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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes different mathematical morphology-based approaches to detect the urban slums from urban buildings using Very High Resolution (VHR) satellite images. Due to the rapid growth of urbanization activities, slum regions are the most evolving regions in South India whose locations are improper in official maps and statistics. Thus, automatic location identification of urban slums provides vital information for urban planners to formulate pro-poor policies and allocate resources properly. To detect the slums automatically from VHR satellite images, Multi Shape-Multi Size-Morphological Profile (MSh-MSi-MP) technique is proposed in this work. The development of this algorithm is motivated by modelling the urban slums using the conventional Morphological Profile (MP) which fails to model different structures in an image. On the other hand, the proposed MSh-MSi-MP is built by the sequence of morphological opening and closing profiles with different shapes and different sizes of structuring elements. Though MSh-MSi-MP yields high accuracy, some open areas in informal settlements and roofline structures of formal settlements are still classified as slums. To optimize these misclassification results, Morphological Spatial Pattern Analysis (MSPA) is used as a post-processing operation. The four different very high-resolution satellite images of Madurai city, South India acquired by WorldView-2 Sensor (1.84 m) proved the ability of the proposed approach to identify urban slums from other features by generating higher classification accuracy than any other methods. [ABSTRACT FROM AUTHOR]