Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. Howeve...

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Bibliographic Details
Main Authors: Lei Pan, Heng-Chao Li, Yang-Jun Deng, Fan Zhang, Xiang-Dong Chen, Qian Du
Format: Article
Language:English
Published: MDPI AG 2017-05-01
Series:Remote Sensing
Subjects:
hyperspectral image
sparse and low-rank graph
tensor
dimensionality reduction
Online Access:http://www.mdpi.com/2072-4292/9/5/452

Internet

http://www.mdpi.com/2072-4292/9/5/452

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