Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India. (28th March 2019)

• Record Type: Journal Article
• Authors: Ranguelova, Elena; Weel, Berend; Roy, Debraj; Kuffer, Monika; Pfeffer, Karin; Lees, Michael
• Is Part Of: European journal of remote sensing. Volume 52(2019)Supplement 1
• Page Start: 40
• Page End: 61
• Publication Date: 2019-03-28
• Subjects: Image segmentation -- informal settlements -- support vector machines -- bag of visual words -- speeded-up robust features; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2018.1535838
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up . For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.