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Classification of Hyperspectral Images Using Machine Learning Methods

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IAENG Transactions on Engineering Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 247))

Abstract

Mixed pixels problem has significant effects on the application of remote sensing images. Spectral unmixing analysis has been extensively used to solve mixed pixels in hyperspectral images. This is based on the knowledge of a set of unidentified endmembers. This study used pixel purity index to extract endmembers from hyperspectral dataset of Washington DC mall. Generalized reduced gradient (GRG) a mathematical optimization method is used to estimate fractional abundances (FA) in the dataset. WEKA data mining tool is chosen to develop ensemble and non-ensemble classifiers using the set of the FA. Random forest (RF) and bagging represent ensemble methods while neural networks and C4.5 represent non-ensemble models for land cover classification (LCC). Experimental comparison between the classifiers shows that RF outperforms all other classifiers. The study resolves the problem associated with LCC by using GRG algorithm with supervised classifiers to improve overall classification accuracy. The accuracy comparison of the learners is important for decision makers in order to consider tradeoffs in accuracy and complexity of methods.

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Acknowledgments

This work was supported by University of Johannesburg, South Africa, Tshwane University of Technology, South Africa and University of the Witwatersrand, Johannesburg. South Africa.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa

    Bolanle Tolulope Abe

  2. Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa

    Bolanle Tolulope Abe

  3. Depatment of Information Technology, Durban University of Technology, Durban, South Africa

    Oludayo O. Olugbara

  4. University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala

Authors
  1. Bolanle Tolulope Abe
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  2. Oludayo O. Olugbara
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  3. Tshilidzi Marwala
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Correspondence to Bolanle Tolulope Abe .

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Editors and Affiliations

  1. Computer & Communication Dean of Engineering College, Catholic University of DaeGu Engineering College, DaeGu, Korea, Republic of (South Korea)

    Haeng Kon Kim

  2. Unit 1, 1/F, International Association of Engineers, Hong Kong, Hong Kong SAR

    Sio-Iong Ao

  3. College of Engineering, California State Polytechnic University, Pomona, California, USA

    Mahyar A. Amouzegar

  4. Abt. Linguistische Datenverarbeitung, Universität Trier Inst.Computerlinguistik, Trier, Germany

    Burghard B. Rieger

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Abe, B.T., Olugbara, O.O., Marwala, T. (2014). Classification of Hyperspectral Images Using Machine Learning Methods. In: Kim, H., Ao, SI., Amouzegar, M., Rieger, B. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 247. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6818-5_39

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  • DOI: https://doi.org/10.1007/978-94-007-6818-5_39

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  • Online ISBN: 978-94-007-6818-5

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