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Unsupervised Learning Method for Mineral Identification from Hyperspectral Data

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Innovations in Bio-Inspired Computing and Applications (IBICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1180))

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Abstract

Hyperspectral imagery is one of the research area in the field of Remote sensing. Hyperspectral sensors record reflectance (also called spectra signature) of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of Remote sensing of Hyperspectral data. EO-1 hyperion dataset is unlabeled data. Various types of clustering algorithms are proposed to identify minerals. In this work principal component analysis is used to reduced it’s dimension by reducing bands. Hard-clustering and soft-clustering algorithms are applied on given data to classify the minerals into classes. K-means is hard type of clustering which classify only non-overlapping cluster however, PFCM is soft type of clustering which allow a data points to belongs more than one cluster. Further, results are compared using cluster validity index using DBI value. Both clustering algorithms are experiments on original HSI image and reduced bands. Result shows that PFCM is perform better than K-means for the both type of images.

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Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    P. Prabhavathy & B. K. Tripathy

  2. Department of Computer Science Engineering, National Institute of Technology, Karnataka, Mangalore, India

    M. Venkatesan

Authors
  1. P. Prabhavathy
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  2. B. K. Tripathy
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  3. M. Venkatesan
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Corresponding author

Correspondence to P. Prabhavathy .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Utkal University, Bhubaneswar, Odisha, India

    Mrutyunjaya Panda

  3. Department of Electronics Engineering, GIET University, Gunupur, Odisha, India

    Subhrajit Pradhan

  4. Area of Project Engineering, University of Cordoba, Córdoba, Córdoba, Spain

    Laura Garcia-Hernandez

  5. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Prabhavathy, P., Tripathy, B.K., Venkatesan, M. (2021). Unsupervised Learning Method for Mineral Identification from Hyperspectral Data. In: Abraham, A., Panda, M., Pradhan, S., Garcia-Hernandez, L., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2019. Advances in Intelligent Systems and Computing, vol 1180. Springer, Cham. https://doi.org/10.1007/978-3-030-49339-4_16

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