Hyperspectral Band Selection Based on Improved Affinity Propagation

Qingyu Zhu, Yulei Wang, Fengchao Wang, Meiping Song, Chein I. Chang

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

4 Citations (Scopus)

Abstract

Dimensionality reduction is a common method to reduce the computational complexity of hyperspectral images and improve the classification performance. Band selection is one of the most commonly used methods for dimensionality reduction. Affinity propagation (AP) is a clustering algorithm that has better performance than traditional clustering methods. This paper proposes an improved AP algorithm (IAP), which divides each intrinsic cluster into several subsets, and combines the information entropy to change the initial availability matrix to obtain a suitable number of clustering results with arbitrary shapes. The experimental results on the public hyperspectral data set show that the band combination selected by IAP has a better classification accuracy compared with all bands data set and band subset by traditional AP algorithm.

Original languageEnglish
Title of host publication2021 11th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665436014
DOIs
Publication statusPublished - 2021 Mar 24
Event11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 - Amsterdam, Netherlands
Duration: 2021 Mar 242021 Mar 26

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2021-March
ISSN (Print)2158-6276

Conference

Conference11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021
Country/TerritoryNetherlands
CityAmsterdam
Period21-03-2421-03-26

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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