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Land Cover Feature Extraction of Multi-spectral Satellite Images Based on Extended Species Abundance Model of Biogeography

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Book cover Transactions on Computational Science XXI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8160))

Abstract

This paper presents a land cover feature extraction technique based on the extended species abundance model of biogeography [15, 18] where we consider the HSI as a function of different combinations of SIVs depending upon the characteristics of the habitat under consideration as an extension to the classical BBO [33, 39]. Making use of the proposed hypotheses, we calculate the HSI of each of the habitats representing the image pixels using two different functions namely entropy and standard deviation and hence maximize the classification efficiency achieved by adapting to dynamic changes in the HSI function definition. The proposed algorithm has been successfully tested on two different multi-spectral satellite image datasets. We also incorporate the above extended model in our previously designed hybrid bio-inspired intelligent classifier [16] and compare its performance with the original hybrid classifier and twelve other classifiers on the 7-Band Alwar Image.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Delhi Technological University (DTU), Delhi, India

    Lavika Goel & Daya Gupta

  2. Defense Terrain & Research Lab, Defense & Research Development Organization (DRDO), Metcalfe House, Delhi, India

    V. K. Panchal

Authors
  1. Lavika Goel
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  2. Daya Gupta
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  3. V. K. Panchal
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Editor information

Editors and Affiliations

  1. University of Calgary, AB, Canada

    Marina L. Gavrilova

  2. CloudFabriQ Ltd., London, UK

    C. J. Kenneth Tan

  3. Technical University of Ostrava, Czech Republic

    Ajith Abraham

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Goel, L., Gupta, D., Panchal, V.K. (2013). Land Cover Feature Extraction of Multi-spectral Satellite Images Based on Extended Species Abundance Model of Biogeography. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-45318-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45317-5

  • Online ISBN: 978-3-642-45318-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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