A greedy modular eigenspace-based band selection approach for hyperspectral imagery

A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2800034 ◽

2018 ◽

Vol 15 (4) ◽

pp. 572-576 ◽

Cited By ~ 10

Author(s):

Peifeng Su ◽

Daizhi Liu ◽

Xihai Li ◽

Zhigang Liu

Keyword(s):

Hyperspectral Imagery ◽

Band Selection ◽

Scale Selection ◽

Selection Approach

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Simulated annealing and greedy modular eigenspaces band selection approach for hyperspectral imagery

10.1117/12.739228 ◽

2007 ◽

Author(s):

Yang-Lang Chang ◽

Jyh-Perng Fang ◽

Hsuan Ren ◽

Wen-Yew Liang ◽

Wei-Lieh Hsu ◽

...

Keyword(s):

Simulated Annealing ◽

Hyperspectral Imagery ◽

Band Selection ◽

Selection Approach

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Simulated annealing band selection approach for hyperspectral imagery

Journal of Applied Remote Sensing ◽

10.1117/1.3502611 ◽

2010 ◽

Vol 4 (1) ◽

pp. 041767 ◽

Cited By ~ 4

Author(s):

Yang-Lang Chang

Keyword(s):

Simulated Annealing ◽

Hyperspectral Imagery ◽

Band Selection ◽

Selection Approach

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A simulated annealing band selection approach for hyperspectral imagery

10.1117/12.685683 ◽

2006 ◽

Cited By ~ 4

Author(s):

Jyh Perng Fang ◽

Yang-Lang Chang ◽

Hsuan Ren ◽

Chun-Chieh Lin ◽

Wen-Yew Liang ◽

...

Keyword(s):

Simulated Annealing ◽

Hyperspectral Imagery ◽

Band Selection ◽

Selection Approach

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A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification

2017 International Symposium on Networks, Computers and Communications (ISNCC) ◽

10.1109/isncc.2017.8072002 ◽

2017 ◽

Author(s):

A. Elmaizi ◽

E. Sarhrouni ◽

A. Hammouch ◽

C. Nacir

Keyword(s):

Information Theory ◽

Support Vector Machine ◽

Hyperspectral Images ◽

Band Selection ◽

Support Vector ◽

Selection Approach

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Towards Weakly Pareto Optimal: An Improved Multi-Objective Based Band Selection Method for Hyperspectral Imagery

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517616 ◽

2018 ◽

Cited By ~ 1

Author(s):

Bin Pan ◽

Liming Wang ◽

Xia Xu ◽

Zhenwei Shi

Keyword(s):

Hyperspectral Imagery ◽

Selection Method ◽

Band Selection ◽

Pareto Optimal ◽

Multi Objective

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Optimum band selection in hyperspectral imagery using swarm intelligence optimization algorithms

2011 International Conference on Image Information Processing ◽

10.1109/iciip.2011.6108925 ◽

2011 ◽

Cited By ~ 5

Author(s):

F. Samadzadegan ◽

F.Tabib Mahmoudi

Keyword(s):

Swarm Intelligence ◽

Hyperspectral Imagery ◽

Optimization Algorithms ◽

Band Selection ◽

Swarm Intelligence Optimization

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Band selection using sparse nonnegative matrix factorization with the thresholded Earth’s mover distance for hyperspectral imagery classification

Earth Science Informatics ◽

10.1007/s12145-014-0201-3 ◽

2015 ◽

Vol 8 (4) ◽

pp. 907-918 ◽

Cited By ~ 9

Author(s):

Weiwei Sun ◽

Weiyue Li ◽

Jialin Li ◽

Yenming Mark Lai

Keyword(s):

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Hyperspectral Imagery ◽

Nonnegative Matrix ◽

Band Selection ◽

Sparse Nonnegative Matrix Factorization

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Linearly constrained band selection for hyperspectral imagery

10.1117/12.665286 ◽

2006 ◽

Cited By ~ 1

Author(s):

Su Wang ◽

Chein-I Chang

Keyword(s):

Hyperspectral Imagery ◽

Band Selection ◽

Linearly Constrained ◽

Selection For

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Investigation of band selection techniques to enable classification of hyperspectral imagery at the edge

Real-Time Image Processing and Deep Learning 2021 ◽

10.1117/12.2585591 ◽

2021 ◽

Author(s):

Samantha Butler ◽

Stanton R. Price ◽

Ali C. Gurbuz ◽

Stephanie J. Price ◽

Joshua R. Fairley

Keyword(s):

Hyperspectral Imagery ◽

Band Selection

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