Sparse coding for spectral signatures in hyperspectral images

Adam Charles, Bruno Olshausen, Christopher J. Rozell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The growing use of hyperspectral imagery lead us to seek automated algorithms for extracting useful information about the scene. Recent work in sparse approximation has shown that unsupervised learning techniques can use example data to determine an efficient dictionary with few a priori assumptions. We apply this model to sample hyperspectral data and show that these techniques learn a dictionary that: 1) contains a meaningful spectral decomposition for hyperspectral imagery, 2) admit representations that are useful in determining properties and classifying materials in the scene, and 3) forms local approximations to the nonlinear manifold structure present in the actual data.

Original languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages191-195
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

Keywords

  • Array Processing and Statistical Signal Processing
  • E.4
  • Remote Sensing

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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