The efficiency of a number of the classical methods of supervised classification of hyperspectral data is estimated by an example of discriminating the types of the underlying surface in natural and man-made areas. The minimum distance, support vector machine, Mahalanobis, and maximum likelihood methods are considered. Particular attention is paid to studying the dependence of the data classification accuracy on the number of spectral features and the way of choosing them in the above-mentioned methods. Experimental results obtained by processing real hyperspectral images of landscapes of various types are reported.
|Journal||Optoelectronics, Instrumentation and Data Processing|
|Publication status||Published - 1 Jan 2016|
- classification of surface types
- hyperspectral images
- reflection spectrum
- remote sensing