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Proceedings Paper

A comparative study on manifold learning of hyperspectral data for land cover classification
Author(s): Ceyda Nur Ozturk; Gokhan Bilgin
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Paper Abstract

This paper focuses on the land cover classification problem by employing a number of manifold learning algorithms in the feature extraction phase, then by running single and ensemble of classifiers in the modeling phase. Manifolds are learned on training samples selected randomly within available data, while the transformation of the remaining test samples is realized for linear and nonlinear methods via the learnt mappings and a radial-basis function neural network based interpolation method, respectively. The classification accuracies of the original data and the embedded manifolds are investigated with several classifiers. Experimental results on a 200-band hyperspectral image indicated that support vector machine was the best classifier for most of the methods, being nearly as accurate as the best classification rate of the original data. Furthermore, our modified version of random subspace classifier could even outperform the classification accuracy of the original data for local Fisher’s discriminant analysis method despite of a considerable decrease in the extrinsic dimension.

Paper Details

Date Published: 4 March 2015
PDF: 7 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431L (4 March 2015); doi: 10.1117/12.2178817
Show Author Affiliations
Ceyda Nur Ozturk, Yildiz Technical Univ. (Turkey)
Gokhan Bilgin, Yildiz Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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