A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery-Reference-Cited by-同舟云学术

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A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery

Published:2019-01 Issue: Volume:53 Page:98-110
ISSN:1389-0417
Container-title:Cognitive Systems Research
language:en
Short-container-title:Cognitive Systems Research

Author:

Yan Qing,Ding Yun,Zhang Jing-Jing,Xia Yi,Zheng Chun-Hou

Funder

National Science Foundation for China

Anhui University

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Experimental and Cognitive Psychology,Software

Reference52 articles.

1. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press.

2. Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations;Bilgin;IEEE Geoscience & Remote Sensing Letters,2008

3. Large scale spectral clustering via landmark-based sparse representation;Cai;IEEE Transactions on Cybernetics,2014

4. A hyperspectral image can predict tropical tree growth rates in single-species stands;Caughlin;Ecological Applications,2016

5. Chang, C. I. (2003). Hyperspectral imaging: Techniques for spectral detection and classification. Plenum Publishing Co.

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