Next Article in Journal
Precision Near-Field Reconstruction in the Time Domain via Minimum Entropy for Ultra-High Resolution Radar Imaging
Next Article in Special Issue
Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network
Previous Article in Journal
Hyperspectral Alteration Information from Drill Cores and Deep Uranium Exploration in the Baiyanghe Uranium Deposit in the Xuemisitan Area, Xinjiang, China
Previous Article in Special Issue
Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 452; doi:10.3390/rs9050452

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

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
Received: 14 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [1335 KB, uploaded 8 May 2017]   |  

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pan, L.; Li, H.-C.; Deng, Y.-J.; Zhang, F.; Chen, X.-D.; Du, Q. Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis. Remote Sens. 2017, 9, 452.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top