Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant AnalysisReportar como inadecuado


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 - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China

2

College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029,China

3

Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA





*

Author to whom correspondence should be addressed.



Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Lenio Soares Galvao and Prasad S. Thenkabail

Abstract 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. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis TSLGDA is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods. View Full-Text

Keywords: hyperspectral image; sparse and low-rank graph; tensor; dimensionality reduction hyperspectral image; sparse and low-rank graph; tensor; dimensionality reduction





Autor: Lei Pan 1, Heng-Chao Li 1,* , Yang-Jun Deng 1, Fan Zhang 2, Xiang-Dong Chen 1 and Qian Du 3

Fuente: http://mdpi.com/



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