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Adaptive Batch Extraction for Hyperspectral Image Classification Based on Convolutional Neural Network

  • Maissa HamoudaEmail author
  • Karim Saheb Ettabaa
  • Med Salim Bouhlel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

Deep Learning for Hyperspectral Imaging Classification is a wonderful solution, despite a few fuzzification. Conventional neural networks are very effective for classification tasks which have allowed them to be used by a very large companies. In this paper, we present an approach to initialize the convolutional data: Firstly, an adaptive selection of kernels by a clustering algorithm; Secondly, by the definition of adaptive batches size. In order to validate our proposed approach, we tested the algorithms on three different hyperspectral images, and the results showed the effectiveness of our proposal.

Keywords

Hyperspectral imaging Feature extraction Convolutional codes Neural networks Machine learning 

Notes

Acknowledgment

This work was supported and financed by the Ministry of Higher Education and Scientific Research of Tunisia.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maissa Hamouda
    • 1
    Email author
  • Karim Saheb Ettabaa
    • 2
  • Med Salim Bouhlel
    • 1
  1. 1.SETITSfaxTunisia
  2. 2.IMT AtlantiqueBrestFrance

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