A survey of methods incorporating spatial information in image classification and spectral unmixing

Le Wang, Chen Shi, Chunyuan Diao, Wenjie Ji, Dameng Yin

Research output: Contribution to journalReview article

Abstract

Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

Original languageEnglish (US)
Pages (from-to)3870-3910
Number of pages41
JournalInternational Journal of Remote Sensing
Volume37
Issue number16
DOIs
StatePublished - Aug 17 2016
Externally publishedYes

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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