Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering

Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering

In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering. This technique consists of three main components: 1) oversegmentation for computing the...

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Bibliographic Details
Main Authors: Yao Qin, Biao Li, Weiping Ni, Sinong Quan, Peizhong Wang, Hui Bian
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Affinity matrix
hyperspectral image (HSI)
nonnegative matrix factorization (NMF)
oversegmentation
remote sensing
spectral clustering
Online Access:https://ieeexplore.ieee.org/document/9268461/

Internet

https://ieeexplore.ieee.org/document/9268461/

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