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Urban Slum Detection Approaches from High-Resolution Satellite Data Using Statistical and Spectral Based Approaches

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Abstract

This paper proposes a new technique to detect the urban slums from urban buildings using very high resolution data. Many cities in the Global South are facing the development and growth of highly dynamic slum areas, but often lack detailed spatial information. Unlike buildings, vegetation and other features, urban slums lack in their unique spectral signatures. Thus, accurate detection of slums using remote sensing data poses real challenge to researchers and decision-makers. In this work, gray-level co-occurrence matrix, Tamura-based statistical feature extraction and wavelet frame transform-based spectral feature extraction techniques are proposed for detecting the urban slums from urban buildings. The very high resolution data of Madurai city, South India, acquired by Worldview-2 sensor (1.84 m) proved the ability of the proposed approaches to identify urban slums from urban buildings. Experimental results demonstrate that the proposed wavelet frame transform-based approach can generate higher classification accuracy than other approaches.

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Acknowledgements

We are very grateful to the provider DigitalGlobe, for providing imagery for research purpose under 8-band proposal scheme.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu, 600119, India

    R. Prabhu

  2. Remote Sensing and GIS Lab, Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, 625015, India

    R. A. Alagu Raja

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  1. R. Prabhu
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  2. R. A. Alagu Raja
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Correspondence to R. Prabhu.

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Prabhu, R., Alagu Raja, R.A. Urban Slum Detection Approaches from High-Resolution Satellite Data Using Statistical and Spectral Based Approaches. J Indian Soc Remote Sens 46, 2033–2044 (2018). https://doi.org/10.1007/s12524-018-0869-9

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  • DOI: https://doi.org/10.1007/s12524-018-0869-9

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