articleIEEE Geoscience and Remote Sensing MagazineApr 29, 2020GREEN OA

Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

BRBehnood RastiDHDanfeng HongRHRenlong HangPGPedram GhamisiXKXudong Kang

Helmholtz-Zentrum Dresden-Rossendorf · Helmholtz Institute Freiberg for Resource Technology · +5 more institutions

Indexed inarxivcrossref

Abstract

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.

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Authors

7
  • BR
    Behnood RastiCorresponding

    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology

  • DH
    Danfeng Hong

    Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

  • RH
    Renlong Hang

    Nanjing University of Information Science and Technology

  • PG
    Pedram Ghamisi

    Helmholtz-Zentrum Dresden-Rossendorf

  • XK
    Xudong Kang

    Artificial Intelligence in Medicine (Canada)

Topics & keywords

Keywords
  • Hyperspectral imaging
  • Toolbox
  • Curse of dimensionality
  • Pattern recognition (psychology)
  • Feature extraction
  • Feature (linguistics)
  • Dimensionality reduction
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