Riemannian manifold learning based k-nearest-neighbor for hyperspectral image classification

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.