Hyperspectral Band Selection Based on Matrix CUR Decomposition

Katherine Henneberger, Longxiu Huang, Jing Qin

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

Abstract

Band selection is an important technique for eliminating spectral redundancy of hyperspectral imagery (HSI) while preserving critical information. Recently, correlations among neighboring bands or pixels have been exploited in the form of graph regularizations to reduce the data dimensionality efficiently. However, manipulation of graph regularizations typically causes computational bottlenecks. In this work, we propose a robust method for hyperspectral band selection based on spatial/spectral graph Laplacians and matrix CUR decomposition. The efficiency of the proposed method has been shown on two real data sets by comparing with several other state-of-the-art band selection methods.

Original languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Pages7380-7383
Number of pages4
ISBN (Electronic)9798350320107
DOIs
StatePublished - 2023
Event2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States
Duration: Jul 16 2023Jul 21 2023

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2023-July

Conference

Conference2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/TerritoryUnited States
CityPasadena
Period7/16/237/21/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Hyperspectral band selection
  • classification
  • matrix CUR decomposition
  • robust PCA

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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