Dimension Reduction of Local Manifold Learning Algorithm for Hyperspectral Image Classification

Abstract

This chapter addresses the problems in hyperspectral image classification by the methods of local manifold learning methods. A manifold is a nonlinear low dimensional subspace that is supported by data samples. Manifolds can be exploited in developing robust feature extraction and classification methods. The manifold coordinates derived from local manifold learning (LLE, LE) methods for multiple data sets. With a proper selection of parameters and a sufficient number of features, the manifold learning methods using the k-nearest neighborhood classification results produced an efficient and accurate data representation that yields higher classification accuracies than linear dimension reduction (PCA) methods for hyperspectral image.