Adaptive sparse representation for hyperspectral image classification

Wei Li, Qian Du

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

10 Citations (Scopus)

Abstract

In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept - a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ1-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ1-norm penalty, named as adaptive SRC (ASRC). ASRC generates more discriminative sparse codes which can represent the testing pixel more robustly. Experimental results demonstrate that the proposed ASRC outperforms the traditional SRC-based classification.

Original languageEnglish
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4955-4958
Number of pages4
ISBN (Electronic)9781479979295
DOIs
Publication statusPublished - 10 Nov 2015
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: 26 Jul 201531 Jul 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2015-November

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/TerritoryItaly
CityMilan
Period26/07/1531/07/15

Keywords

  • Hyperspectral imagery
  • pattern classification
  • sparse representation

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