Hyperspectral Classification Using Low Rank and Sparsity Matrices Decomposition

Hongju Cao, Xiaodi Shang, Chunyan Yu, Meiping Song, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Classification is a major task in hyperspectral image (HSI) processing. This paper develops an approach by taking advantage of low rank matrix derived from the low rank and sparse matrix decomposition (LRSMD) model which decomposes a hyperspectral data matrix X as X = L+S+n where L, S and n are referred to low rank, sparse and noise matrices respectively. The hyperspectral image classification (HSIC) is then performed on the low rank matrix L rather than the original data matrix X where the well-known go decomposition (GoDec) is used to produce such LRSMD model. To determine the two key parameters used in GoDec, the rank of L, m, and the cardinality of the sparse matrix, k the well-known virtual dimensionality (VD) and minimax-singular value decomposition (MX-SVD) methods are used for this purpose. Finally, to demonstrate advantages of using the low rank matrix L, support vector machine (SVM) and an edge-preserving filters (EPF)-based classifiers are implemented to evaluate classification performance.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages477-480
Number of pages4
ISBN (Electronic)9781728163741
DOIs
Publication statusPublished - 2020 Sept 26
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: 2020 Sept 262020 Oct 2

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period20-09-2620-10-02

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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