Classification of Hyperspectral Images with Different Methods of Training Set Formation
- 作者: Borzov S.M.1, Potaturkin O.I.1,2
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隶属关系:
- Institute of Automation and Electrometry, Siberian Branch
- Novosibirsk State University
- 期: 卷 54, 编号 1 (2018)
- 页面: 76-82
- 栏目: Analysis and Synthesis of Signals and Images
- URL: https://journals.rcsi.science/8756-6990/article/view/212366
- DOI: https://doi.org/10.3103/S8756699018010120
- ID: 212366
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详细
The efficiency of the methods of controlled spectral and spectral-spatial classification of vegetation types on the basis of hyperspectral pictures with different methods of training set formation is evaluated. The dependence of the classification accuracy on the number of spectral features is considered. It is shown that simultaneous allowance for spatial and spectral features ensures highquality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.
作者简介
S. Borzov
Institute of Automation and Electrometry, Siberian Branch
编辑信件的主要联系方式.
Email: borzov@iae.nsk.su
俄罗斯联邦, pr. Akademika Koptyuga 1, Novosibirsk, 630090
O. Potaturkin
Institute of Automation and Electrometry, Siberian Branch; Novosibirsk State University
Email: borzov@iae.nsk.su
俄罗斯联邦, pr. Akademika Koptyuga 1, Novosibirsk, 630090; ul. Pirogova 2, Novosibirsk, 630090
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