Learning a local manifold representation based on improved neighborhood rough set and LLE for hyperspectral dimensionality reduction

Abstract

Hyperspectral data with high dimensionality always needs more storage space and increases the computational consumption, manifold learning based dimensionality reduction method is a popular way to meet these requirements. Local manifold learning consists of two vital steps: neighbors selection and weights computation, and the former is critical to the accuracy of the result. In this paper, an improved neighborhood rough set (INRS) and local linear embedding (LLE) are proposed for local neighbors selection, thus INRSLLE is proposed. Firstly, neighbor candidates are selected using Euclidean distance and spectral distance, then a parameter undirected graph is constructed from original data to improve the anti-noise ability. INRS improved by this parameter undirected graph can select enough neighbors from these candidates, which increases the contribution of neighbors to corresponding samples. Finally, LLE is used to learn local manifold representation. Results of the proposed method and comparison methods like NMF and LP-KSVD have been classified by four different classifiers respectively. Experimental results performed over two real-world hyperspectral datasets indicate the proposed INRSLLE not only considers the spectral-spatial information of hyperspectral data, but also selects more suitable neighbors on local manifolds and increases the anti-noise ability.