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Classification of Hyperspectral Imagery Using Random Forest

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Smart and Innovative Trends in Next Generation Computing Technologies (NGCT 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 827))

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Abstract

In this paper the classification of hyperspectral images is investigated by using a supervised approach. The spectral feature are extracted with well known decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE) techniques. The most informative features are fed to random forest (RF) classifier to perform pixel-wise classification. The experiments are carried out on two benchmark hyperspectral images. The results show that RF classifier generates good classification accuracies for hyperspectral image with smaller execution time. Among feature extraction techniques, DBFE has produced better results than NWFE.

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Acknowledgement

Authors are very thankful to Dr. David Landgrebe, Purdue University, for providing Multispec tool and Dr. Paolo Gamba, Professor at PU, Italy, for providing Hyperspectral dataset used in this research work.

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Authors and Affiliations

  1. Uttarakhand Technical University, Dehradun, India

    Diwaker Mourya

  2. Birla Institute of Applied Sciences, Bhimtal, India

    Ashutosh Bhatt

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  1. Diwaker Mourya
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  2. Ashutosh Bhatt
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Correspondence to Diwaker Mourya .

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Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Mourya, D., Bhatt, A. (2018). Classification of Hyperspectral Imagery Using Random Forest. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_5

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  • DOI: https://doi.org/10.1007/978-981-10-8657-1_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8656-4

  • Online ISBN: 978-981-10-8657-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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