We propose and investigate a complex hyperspectral image classification method with regard to the spatial proximity of pixels. Key feature of the method is that it uses common and relatively simple algorithms to attain high accuracy. The method combines the results of pixel-wise support vector machine classification and a set of contours derived from kmeans++ image clustering. To prevent redundant processing of similar data a principal component analysis is used. The method proposed enables the accuracy and speed of hyperspectral image classification to be enhanced.

Date Published: 25 March 2015
PDF: 9 pages
Proc. SPIE 9533, Optical Technologies for Telecommunications 2014, 95330M (25 March 2015); doi: 10.1117/12.2180543

Published in SPIE Proceedings Vol. 9533:
Optical Technologies for Telecommunications 2014
Vladimir A. Andreev; Vladimir A. Burdin; Albert H. Sultanov; Oleg G. Morozov, Editor(s)