Global spatial and local spectral similarity-based group sparse representation for hyperspectral imagery classification

Haoyang Yu, Lianru Gao, Wenzhi Liao, Paolo Gamba, Bing Zhang

Research output: Contribution to conferencePaper

Abstract

Spectral-spatial classification has been widely exploited for hyperspectral imagery. However, current methods either focus on local spatial similarity or global nonlocal self-similarity (NLSS). In this paper, we propose novel methods to couple both global spatial similarity and local spectral similarity together in a single framework. In particular, our approaches exploit global spatial similarity by searching non-overlap nonlocal patches, whereas spectral similarity is determined locally within the found patches. Experimental results on two real hyperspectral data sets demonstrate the efficiency of the proposed methods, with 5%-7% (overall classification accuracy) improvements over approaches that only consider either global or local similarity.
Original languageEnglish
Pages3579-3582
Number of pages4
DOIs
Publication statusPublished - 5 Nov 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • hyperspectral image
  • classification
  • nonlocal self-similarity
  • group sparse representation
  • support vector machines
  • spatial coherence
  • geophysical image processing

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