Fuzzy Markov chains approach to feature selection for high dimensional remote sensing data

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) ◽

10.1109/igarss.2001.978337 ◽

2002 ◽

Cited By ~ 1

Author(s):

S. Yu ◽

P. Scheunders

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Markov Chains ◽

Remote Sensing Data ◽

High Dimensional ◽

Sensing Data ◽

Selection For

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Feature selection for high-dimensional remote sensing data by maximum entropy principle based optimization

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) ◽

10.1109/igarss.2001.978336 ◽

2002 ◽

Author(s):

Shixin Yu ◽

P. Scheunders

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Maximum Entropy ◽

Maximum Entropy Principle ◽

Remote Sensing Data ◽

High Dimensional ◽

Sensing Data ◽

Entropy Principle ◽

Selection For

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FODSPO based feature selection for hyperspectral remote sensing data

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) ◽

10.1109/whispers.2014.8077613 ◽

2014 ◽

Author(s):

Pedram Ghamisi ◽

Micael S. Couceiro ◽

Jon Atli Benediktsson

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Sensing Data ◽

Selection For

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Feature selection for remote-sensing data classification

10.1117/12.196757 ◽

1994 ◽

Author(s):

Sebastiano B. Serpico ◽

Paolo Pellegretti ◽

Lorenzo Bruzzone

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Remote Sensing Data ◽

Data Classification ◽

Sensing Data ◽

Selection For

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Optimal band selection for high dimensional remote sensing data using genetic algorithm

10.1117/12.847907 ◽

2009 ◽

Cited By ~ 5

Author(s):

Xianfeng Zhang ◽

Quan Sun ◽

Jonathan Li

Keyword(s):

Remote Sensing ◽

Genetic Algorithm ◽

Remote Sensing Data ◽

High Dimensional ◽

Band Selection ◽

Sensing Data ◽

Selection For

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Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for high-dimensional remote sensing data

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166) ◽

10.1109/icsmc.2000.886392 ◽

2002 ◽

Cited By ~ 9

Author(s):

S. Yu ◽

S. De Backer ◽

P. Scheunders

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Nearest Neighbor ◽

Remote Sensing Data ◽

High Dimensional ◽

Sensing Data ◽

Genetic Feature ◽

Nearest Neighbor Classifiers ◽

Genetic Feature Selection

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Band selection for high-dimensional remote sensing data by mutual information

2016 International Conference on Electrical and Information Technologies (ICEIT) ◽

10.1109/eitech.2016.7519625 ◽

2016 ◽

Author(s):

Ibtissam Banit'ouagua ◽

Mounir Ait Kerroum ◽

Ahmed Hammouch ◽

Driss Aboutajdine

Keyword(s):

Remote Sensing ◽

Mutual Information ◽

Remote Sensing Data ◽

High Dimensional ◽

Band Selection ◽

Sensing Data ◽

Selection For

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Recognition of High Dimensional Multi-Sensor Remote Sensing Data of Various Spatial Resolution

2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP) ◽

10.1109/dsmp47368.2020.9204186 ◽

2020 ◽

Author(s):

Volodymyr Hnatushenko ◽

Viktoriia Hnatushenko

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Remote Sensing Data ◽

High Dimensional ◽

Sensing Data

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Evaluation of five feature selection methods for remote sensing data

10.1117/12.441584 ◽

2001 ◽

Cited By ~ 2

Author(s):

Aniati Murni ◽

S. Mulyono ◽

Dina Chahyati

Keyword(s):

Remote Sensing ◽

Feature Selection ◽

Remote Sensing Data ◽

Selection Methods ◽

Sensing Data

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Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks

International Journal of Remote Sensing ◽

10.1080/014311697218863 ◽

1997 ◽

Vol 18 (4) ◽

pp. 971-979 ◽

Cited By ~ 42

Author(s):

Y.-Q. Jin ◽

C. Liu

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Artificial Neural Networks ◽

Remote Sensing Data ◽

High Dimensional ◽

Sensing Data ◽

Passive Remote Sensing ◽

Artificial Neural

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Genetic algorithm based new sequence of principal component regression (GA-NSPCR) for feature selection and yield prediction using hyperspectral remote sensing data

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6351743 ◽

2012 ◽

Cited By ~ 3

Author(s):

Sidik Mulyono ◽

Mohamad Ivan Fanany ◽

T Basaruddin

Keyword(s):

Remote Sensing ◽

Genetic Algorithm ◽

Feature Selection ◽

Principal Component Regression ◽

Remote Sensing Data ◽

Principal Component ◽

Hyperspectral Remote Sensing ◽

Yield Prediction ◽

Sensing Data

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