Journal
2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Volume -, Issue -, Pages 1975-1978Publisher
IEEE
DOI: 10.1109/IGARSS.2013.6723195
Keywords
hyperspectral image; classification; feature extraction; Riemannian manifold learning; k nearest neighbors
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The existence of nonlinear characteristics in hyperspectral data is considered as an influential factor curtailing the classification accuracy of canonical linear classifier like k-nearest neighbor (k-NN). To deal with the problem, we investigated approaches to combine manifold learning methods and the k-NN classifier to preserve nonlinear characteristics contained in hyperspectral imagery. Then we proposed a Riemannian manifold learning (RML) based k-NN classifier for hyperspectral image classification, which substitutes the Euclidean distances used in canonical kNN by geodesic distances yielded by RML. The experimental results on AVIRIS data show that in most cases, the RML-kNN Classifier accesses higher classification accuracies than canonical k-NN.
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