Share Email Print

Proceedings Paper

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data
Author(s): A. O. Dementev; E. V. Dmitriev; V. V. Kozoderov; V. D. Egorov
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

Paper Details

Date Published: 5 October 2017
PDF: 11 pages
Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 104281M (5 October 2017); doi: 10.1117/12.2278074
Show Author Affiliations
A. O. Dementev, Institute of Numerical Mathematics (Russian Federation)
E. V. Dmitriev, Institute of Numerical Mathematics (Russian Federation)
V. V. Kozoderov, M.V. Lomonosov Moscow State Univ. (Russian Federation)
V. D. Egorov, Institute of Numerical Mathematics (Russian Federation)

Published in SPIE Proceedings Vol. 10428:
Earth Resources and Environmental Remote Sensing/GIS Applications VIII
Ulrich Michel; Karsten Schulz, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?