A comparative study on manifold learning of hyperspectral data for land cover classification

Ceyda Nur Ozturk; Gokhan Bilgin

doi:10.1117/12.2178817

4 March 2015 A comparative study on manifold learning of hyperspectral data for land cover classification

Ceyda Nur Ozturk, Gokhan Bilgin

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94431L (2015) https://doi.org/10.1117/12.2178817
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

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.

Citation Download Citation

Ceyda Nur Ozturk and Gokhan Bilgin "A comparative study on manifold learning of hyperspectral data for land cover classification", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431L (4 March 2015); https://doi.org/10.1117/12.2178817

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