Clustering Hyperspectral Imagery via Sparse Representation Features of the Generalized Orthogonal Matching Pursuit

Clustering Hyperspectral Imagery via Sparse Representation Features of the Generalized Orthogonal Matching Pursuit

This study focused on improving the clustering performance of hyperspectral imaging (HSI) by employing the Generalized Orthogonal Matching Pursuit (GOMP) algorithm for feature extraction. Hyperspectral remote sensing imaging technology, which is crucial in various fields like environmental monitorin...

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Bibliographic Details
Main Authors: Wenqi Guo, Xu Xu, Xiaoqiang Xu, Shichen Gao, Zibu Wu
Format: Article
Language:English
Published: MDPI AG 2024-08-01
Series:Remote Sensing
Subjects:
hyperspectral imaging
generalized orthogonal matching pursuit
feature extraction
clustering
sparse representation
Online Access:https://www.mdpi.com/2072-4292/16/17/3230

Internet

https://www.mdpi.com/2072-4292/16/17/3230

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