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Segmented-Truncated-SVD for Effective Feature Extraction in Hyperspectral Image Classification

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Machine Intelligence and Emerging Technologies (MIET 2022)

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Abstract

Hyperspectral images (HSIs) are typically developed to obtain essential details about the land captured by hundreds of tiny and spectral bands. The classification does not achieve the desired performance using HSI dataset due to a large number of bands. Techniques for band reduction are utilized for the enhancement of classification performance. In this work, we propose an improved Truncated Singular Value Decomposition (TSVD), a classical feature extraction method, based on the supremacy of band segmentation in the HSI analysis. We call our method as Segmented-Truncated-SVD (STSVD), where the TSVD application extracts better local intrinsic and global properties from the HSI. Rather than applying the full dataset, we first segment the entire dataset into a number of strongly correlated spectral band subgroups and apply the TSVD on each subgroup separately. For Per-pixel classification by support vector machine (SVM) STSVD method, the classical PCA, Segmented-PCA (SPCA), SVD, and TSVD methods are applied to the mixed agricultural Indian Pines HSI dataset. The experimental results exhibit that the overall classification of STSVD (87.373%) remarkably outperforms all the other investigated methods: PCA (83.554%), SPCA (86.774%), SVD (83.986%), TSVD (83.526%), and the complete dataset without employing any feature reduction method (82.997%). The proposed STSVD also demands the least space complexities in different phases.

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Authors and Affiliations

  1. Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

    Md. Moshiur Rahman, Shabbir Ahmed, Md. Shahriar Haque, Md. Abu Marjan, Masud Ibn Afjal & Md. Palash Uddin

Authors
  1. Md. Moshiur Rahman
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  2. Shabbir Ahmed
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  3. Md. Shahriar Haque
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  4. Md. Abu Marjan
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  5. Masud Ibn Afjal
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  6. Md. Palash Uddin
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Corresponding author

Correspondence to Md. Abu Marjan .

Editor information

Editors and Affiliations

  1. Noakhali Science and Technology University, Noakhali, Bangladesh

    Md. Shahriare Satu

  2. The University of Queensland, St. Lucia, QLD, Australia

    Mohammad Ali Moni

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Daffodil International University, Dhaka, Bangladesh

    Mohammad Shamsul Arefin

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Rahman, M.M., Ahmed, S., Haque, M.S., Marjan, M.A., Afjal, M.I., Uddin, M.P. (2023). Segmented-Truncated-SVD for Effective Feature Extraction in Hyperspectral Image Classification. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_42

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  • DOI: https://doi.org/10.1007/978-3-031-34622-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34621-7

  • Online ISBN: 978-3-031-34622-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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