Transferability of Object-Oriented Image Analysis Methods for Slum Identification

Remote Sensing

Divyani Kohli¹  Pankaj Warwadekar²  Norman Kerle²  Richard Sliuzas² 

[1] Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, Enschede AE 7514, The Netherlands;

关键词: slums;    ontology;    remote sensing;    image classification;    texture;    object-oriented image analysis (OOA);    Ahmedabad/India;   

DOI  :  10.3390/rs5094209

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland