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Identification of Impervious Built-Up Surface Features Using ResourceSat-2 LISS-III-Based Novel Optical Built-Up Index

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Remote Sensing and GIScience

Abstract

In the context of urban planning, the increasing urban concentration and growth result in changes from natural landscape to impervious surface features. Remote sensing provides an efficient method in automated identification of land use/cover classes. However, a common challenge is the accurate extraction of built-up features from satellite images. The conventional Normalized Difference Built-up Index (NDBI) has been modified by several researchers in the anticipation of improvement of the built-up area classification. The indices adopted in the study are Index-based Built-up Index (IBI), Built-up Index (BUI), NDBI, and the newly developed Impervious Built-up Index (IBUI). These indices work on automated kernel-based probabilistic thresholding algorithm to group the index values into built-up and non-built-up areas. This study investigates the performance of the abovementioned spectral indices on ResourceSat-2 Linear Imaging Self-Scanner-III (LISS III) imageries of the city of Kolkata, India, and its adjoining areas in the delineation of built-up areas and compares them based on spectral feature space correlation and classification approach. Although all the built-up indices showed high mutual correlation, the performance varied greatly as showed by the accuracy in the classification. Overall accuracy values of built-up feature extraction using IBUI, IBI, BUI, and NDBI are 92.33%, 89%, 86%, and 80.67% respectively.

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Acknowledgements

Authors express sincere gratitude to National Remote Sensing Centre (NRSC), Government of India for providing images and Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India, for providing funds under SERB EMR scheme (File Number: EMR/2017/002838).

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Authors and Affiliations

  1. Department of Civil Engineering, Haldia Institute of Technology, Haldia, India

    Abhisek Santra, Shreyashi Santra Mitra, Suman Sinha, Shidharth Routh & Akhilesh Kumar

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  1. Abhisek Santra
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  2. Shreyashi Santra Mitra
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  3. Suman Sinha
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  4. Shidharth Routh
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  5. Akhilesh Kumar
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Correspondence to Abhisek Santra .

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Editors and Affiliations

  1. College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India

    Pavan Kumar

  2. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

    Haroon Sajjad

  3. Department of Geophysics, Kurukshetra University, Kurukshetra, Haryana, India

    Bhagwan Singh Chaudhary

  4. Remote Sensing and GIS, Kumaun University, Almora, Uttarakhand, India

    J. S. Rawat

  5. Department of Geography, Kumaun University, Nainital, Uttarakhand, India

    Meenu Rani

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Santra, A., Mitra, S.S., Sinha, S., Routh, S., Kumar, A. (2021). Identification of Impervious Built-Up Surface Features Using ResourceSat-2 LISS-III-Based Novel Optical Built-Up Index. In: Kumar, P., Sajjad, H., Chaudhary, B.S., Rawat, J.S., Rani, M. (eds) Remote Sensing and GIScience . Springer, Cham. https://doi.org/10.1007/978-3-030-55092-9_7

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