Two Stage SVM Classification for Hyperspectral Data

Michal Cholewa, Przemyslaw Glomb


In this article, we present a method of enhancing the SVM classification of hyperspectral data with the use of three supporting classifiers. It is done by applying the fully trained classifiers on learning set to obtain the pattern of their behavior which then can be used for refinement of classifier construction. The second stage either is a straightforward translation of first stage, if the first stage classifiers agree on the result, or it consists of using retrained SVM classifier with only the data from learning data selected using first stage. The scheme shares some features with committee of experts fusion scheme, yet it clearly distinguishes lead classifier using the supporting ones only to refine its construction. We present the construction of two-stage scheme, then test it against the known Indian Pines HSI dataset and test it against straightforward use of SVM classifier, over which our method achieves noticeable improvement.


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Paper Citation

in Harvard Style

Cholewa M. and Glomb P. (2016). Two Stage SVM Classification for Hyperspectral Data . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 387-391. DOI: 10.5220/0005828103870391

in Bibtex Style

author={Michal Cholewa and Przemyslaw Glomb},
title={Two Stage SVM Classification for Hyperspectral Data},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Two Stage SVM Classification for Hyperspectral Data
SN - 978-989-758-173-1
AU - Cholewa M.
AU - Glomb P.
PY - 2016
SP - 387
EP - 391
DO - 10.5220/0005828103870391