An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images

An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images

Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement to spectral information, but related research...

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Bibliographic Details
Main Authors: Zhengyi Chen, Chunmin Zhang, Tingkui Mu, Tingyu Yan, Zeyu Chen, Yanqiang Wang
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Remote Sensing
Subjects:
hyperspectral images
polarization
subspace clustering
sparse representation
Online Access:https://www.mdpi.com/2072-4292/11/13/1513

Internet

https://www.mdpi.com/2072-4292/11/13/1513

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