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Remote Sens. 2013, 5(9), 4209-4228; doi:10.3390/rs5094209

Transferability of Object-Oriented Image Analysis Methods for Slum Identification

Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, Enschede AE 7514, The Netherlands
Author to whom correspondence should be addressed.
Received: 30 June 2013 / Revised: 21 August 2013 / Accepted: 22 August 2013 / Published: 29 August 2013
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Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed. View Full-Text
Keywords: slums; ontology; remote sensing; image classification; texture; object-oriented image analysis (OOA); Ahmedabad/India slums; ontology; remote sensing; image classification; texture; object-oriented image analysis (OOA); Ahmedabad/India

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kohli, D.; Warwadekar, P.; Kerle, N.; Sliuzas, R.; Stein, A. Transferability of Object-Oriented Image Analysis Methods for Slum Identification. Remote Sens. 2013, 5, 4209-4228.

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