Joint low rank and sparse representation-based hyperspectral image classification

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

5 Citations (Scopus)

Abstract

Representation-based classification has gained great interest recently. In this paper, we present a novel joint low rank and sparse representation-based classification (JLRSRC) method for hyperspectral imagery. For a testing set, JLRSRC seeks weight coefficients to represent a testing pixel as linear combination of atoms in an over-complete dictionary. Since the low rank model is capable of preserving global data structures of data while sparsity can select the discriminative neighbors in the feature space, the resulting representation is both representative and discriminative. Experimental results demonstrate the effectiveness of the proposed JLRSRC when compared with the traditional counterparts.

Original languageEnglish
Title of host publication2016 8th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509006083
DOIs
Publication statusPublished - 28 Jun 2016
Externally publishedYes
Event8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 - Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume0
ISSN (Print)2158-6276

Conference

Conference8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Country/TerritoryUnited States
CityLos Angeles
Period21/08/1624/08/16

Keywords

  • Hyperspectral imagery
  • Low rank representation
  • Pattern classification
  • Sparse representation

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