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

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.

• Publication year

  2020

• Venue

  IEEE Geoscience and Remote Sensing Magazine

• Publication date

  2020-03-05

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/MGRS.2020.2979764arXiv 2003.02822
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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