An efficient spatial-spectral classification method for hyperspectral imagery

Wei Li, Qian Du

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

In this paper, a feature extraction method using a very simple local averaging filter for hyperspectral image classification is proposed. The method potentially smoothes out trivial variations as well as noise of hyperspectral data, and simultaneously exploits the fact that neighboring pixels tend to belong to the same class with high probability. The spectral-spatial features, which are extracted and fed into a following classifier with locality preserving character in the experimental setup, are compared with other features, such as spectral only and wavelet-features. Simulated results show that the proposed approach facilitates superior discriminant features extraction, thereby yielding significant improvement in hyperspectral image classification performance.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communications, and Processing X
PublisherSPIE
ISBN (Print)9781628410617
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventSatellite Data Compression, Communications, and Processing X - Baltimore, MD, United States
Duration: 8 May 20149 May 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9124
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSatellite Data Compression, Communications, and Processing X
Country/TerritoryUnited States
CityBaltimore, MD
Period8/05/149/05/14

Keywords

  • Feature extraction
  • Hyperspectral imagery
  • Image classification

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