Siberian Federal University 
- Issue
- Journal of Siberian Federal University. Engineering & Technologies. 2016 9 (7)
- Authors
- Dmitriev, Egor V.; Kozoderov, Vladimir V.
- Contact information
- Dmitriev, Egor V.: Institute of Numerical Mathematics RAS 8 Gubkina Str., Moscow, 119333, Russia; Moscow Institute for Physics and Technology (State University) 9 Institutskiy per., Dolgoprudny, 141700, Russia; E-mail: ; Kozoderov, Vladimir V.: M.V. Lomonosov Moscow State University 1 Leninskiye Gory, Moscow, 119991, Russia
- Keywords
- remote sensing; pattern recognition; spectral classification; hyperspectral measurements
- Abstract
The performance of the spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. The characteristic features of metric classifiers, parametric Bayesian classifiers and multiclass support vector machines are discussed. The results of classification of hyperspectral airborne images by using the specified above methods and comparative analysis are demonstrated. The advantages of the use of nonlinear classifiers are shown. It is also shown, the similarity of the results of some modifications of support vector machines and Bayesian classification
- Pages
- 1001-1011
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/28079
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).