Инд. авторы:	Borzov S.M., Potaturkin O.I.
Заглавие:	Classification of Hyperspectral Images with Different Methods of Training Set Formation
Библ. ссылка:	Borzov S.M., Potaturkin O.I. Classification of Hyperspectral Images with Different Methods of Training Set Formation // Optoelectronics, Instrumentation and Data Processing. - 2018. - Vol.54. - Iss. 1. - P.76-82. - ISSN 8756-6990. - EISSN 1934-7944.
Внешние системы:	DOI: 10.3103/S8756699018010120; WoS: 000429119400012;
Реферат:	eng: 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.
Ключевые слова:	spectral and spatial features; hyperspectral image; remote sensing; classification of surface types; SPECTRAL-SPATIAL CLASSIFICATION;
Издано:	2018
Физ. характеристика:	с.76-82