Machine learning classification methods in hyperspectral data processing for agricultural applications

Author:

Hruška Jonáš1,Adão Telmo2,Pádua Luís1,Marques Pedro1,Cunha António2,Peres Emanuel2,Sousa António2,Morais Raul2,Sousa Joaquim J.2

Affiliation:

1. University of Trás-os-Montes e Alto, Douro, Quinta de Prados, Vila Real

2. University of Trás-os-Montes e, Alto Douro, Quinta de Prados, Vila Real

Funder

European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI)

Publisher

ACM

Reference47 articles.

1. Sabale S.P. and Jadhav C.R. 2015. Hyperspectral Image Classification Methods in Remote Sensing - A Review. 2015InternationalConferenceonComputing Communication Control and Automation (Feb. 2015) 679--683. 10.1109/ICCUBEA.2015.139 Sabale S.P. and Jadhav C.R. 2015. Hyperspectral Image Classification Methods in Remote Sensing - A Review. 2015InternationalConferenceonComputing Communication Control and Automation (Feb. 2015) 679--683. 10.1109/ICCUBEA.2015.139

2. Survey of supervised classification techniques for hyperspectral images

3. Advanced Spectral Classifiers for Hyperspectral Images: A review

4. Tarabalka Y. 2010. Classification of hyperspectral data using spectral-spatial approaches. Institut National Polytechnique de Grenoble-INPG. Tarabalka Y. 2010. Classification of hyperspectral data using spectral-spatial approaches . Institut National Polytechnique de Grenoble-INPG.

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