Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques

Wei Li, Fubiao Feng, Hengchao Li, Qian Du

Research output: Contribution to journalReview articlepeer-review

73 Citations (Scopus)

Abstract

Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral signatures, making it possible to distinguish materials through subtle spectral discrepancies. Because these spectral bands are highly correlated, dimensionality reduction, as the name suggests, seeks to reduce data dimensionality without losing desirable information. This article reviews discriminant analysisbased dimensionality-reduction approaches for hyperspectral imagery, including typical linear discriminant analysis (LDA), state-of-the-art sparse graph-based discriminant analysis (SGDA), and their extensions.

Original languageEnglish
Pages (from-to)15-34
Number of pages20
JournalIEEE Geoscience and Remote Sensing Magazine
Volume6
Issue number1
DOIs
Publication statusPublished - Mar 2018
Externally publishedYes

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