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Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification

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Abstract

Hyperspectral imaging is highly important with respect to the detection, identification and classification of various natural resources—minerals, earth’s natural eruptions, vegetation and related man-made materials and other existing backgrounds. In this study, a novel deep learning-based fuzzy-twin proximal support vector machine (DL-FTPSVM) kernel neural network model is proposed to perform an effective hyperspectral image (HSI) classification. The modelled new DL-FTPSVM is designed to handle the irregularities and existing training complexities of the present hyperspectral image classifier models. A mapreduce framework is introduced for analysing the large volumes of hyperspectral images by partitioning these images into few portions based on mapper and reducer functions. The fuzzy-twin proximal support vector machine neural model is designed with the fuzzy twin hyperplanes comprising of Gaussian kernel, Bessel kernel, ANOVA radial basis function (RBF) kernel and linear spline kernel and a simple triangular fuzzy function is employed to construct the twin hyperplanes for HSI classification. Deep learning (DL) framework supports the new fuzzy-twin proximal SVM kernels to learn more effectively with the auto-encoder and decoder mechanism. For testing and validating the proposed DL-FTPSVM neural network model, the test beds are the Kennedy Space Centre datasets, University of Pavia datasets, Salinas datasets and Indian Pine datasets and performance metrics are evaluated. Results evaluated prove the superiority of the DL-FTPSVM model incurring better classification accuracy compared to techniques from previous works. The randomness of the proposed classifier is handled with the statistical analysis for the HSI classification.

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Authors and Affiliations

  1. Anna University, Chennai, Tamil Nadu, 600 025, India

    Sanaboina Leela Krishna

  2. Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, 641 032, India

    I. Jasmine Selvakumari Jeya

  3. Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh, 791113, India

    S. N. Deepa

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  1. Sanaboina Leela Krishna
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Correspondence to I. Jasmine Selvakumari Jeya.

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Krishna, S.L., Jeya, I.J.S. & Deepa, S.N. Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification. Neural Comput & Applic 34, 19343–19376 (2022). https://doi.org/10.1007/s00521-022-07517-6

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