Hyperspectral Band Selection based on Improved Affinity Propagation

Thumbnail Image

Files

WHISPERS_2021_paper_21.pdf (833.21 KB)

Links to Files

http://www.ieee-whispers.com/wp-content/uploads/2021/03/WHISPERS_2021_paper_21.pdf

Permanent Link

http://hdl.handle.net/11603/21742

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2021

Type of Work

conference papers and proceedings preprints
Text

Department

Program

Citation of Original Publication

Zhu, Qingyu et al; Hyperspectral Band Selection based on Improved Affinity Propagation; 11 Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2021; http://www.ieee-whispers.com/wp-content/uploads/2021/03/WHISPERS_2021_paper_21.pdf

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

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.