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Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification

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Abstract

The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction.

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Authors and Affiliations

  1. Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India

    Gopal Krishna & Rabi N. Sahoo

  2. Geo-Informatics Laboratory, Division of Sample Surveys, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, India

    Gopal Krishna, Tauqueer Ahmad & Prachi M. Sahoo

  3. ICAR-Indian Institute of Water Management, Bhubaneswar, Odisha, India

    Sanatan Pradhan

Authors
  1. Gopal Krishna
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  2. Rabi N. Sahoo
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  3. Sanatan Pradhan
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  4. Tauqueer Ahmad
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  5. Prachi M. Sahoo
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Correspondence to Gopal Krishna.

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Communicated by: H. A. Babaie

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Krishna, G., Sahoo, R.N., Pradhan, S. et al. Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification. Earth Sci Inform 11, 159–170 (2018). https://doi.org/10.1007/s12145-017-0324-4

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  • DOI: https://doi.org/10.1007/s12145-017-0324-4

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