CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH DIFFERENT METHODS OF TRAINING SET FORMATION
S. M. Borzov1, O. I. Potaturkin1,2
1Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, prosp. Akademika Koptyuga, 1
2Novosibirsk State University, 630090, Novosibirsk, ul. Pirogova, 2
Keywords: дистанционное зондирование Земли, гиперспектральные изображения, классификация типов поверхностей, спектральные и пространственные признаки, remote sensing, hyperspectral image, classification of surface types, spectral and spatial features
Abstract
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 high-quality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.
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