Deep Learning Ensemble Model for Hyperspectral Image Classification

Authors

  • Parul Bhanarkar  Babasaheb Naik College of Engineering, Pusad, Maharashtra, India
  • Dr. Salim Y. Amdani  Babasaheb Naik College of Engineering, Pusad, Maharashtra, India

Keywords:

Ensemble , Classifier, hyperspectral , images, convolution, neural network.

Abstract

Hyperspectral image catch exceptionally increasing data dimensionality about filtered objects and, henceforth, can be utilized to uncover different attributes of the materials present in the broke down scene. In any case, such picture information are hard to move because of their huge volume, and creating new ground-truth datasets that could be used to prepare regulated students is expensive, tedious, very userdependent, and regularly infeasible practically speaking. The examination endeavors have been zeroing in on creating calculations for hyperspectral information order and unmixing, which are two primary assignments in the investigation chain of such symbolism. Albeit in the two of them, the profound learning strategies have blossomed as a very viable apparatus, planning the profound models that sum up above and beyond the inconspicuous information is a not kidding viable test in arising applications. In this paper, we present the profound outfits profiting from various structural advances of convolution base models also propose another methodology towards totaling the results of base students utilizing an administered fuser

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-03-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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How to Cite

[1]
Parul Bhanarkar, Dr. Salim Y. Amdani, " Deep Learning Ensemble Model for Hyperspectral Image Classification, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.12-18, March-April-2022.