Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images
journal contributionposted on 11.05.2018, 15:29 by Jinliang An, Xiangrong Zhang, Huiyu Zhou, Licheng Jiao
Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the sub-manifolds information of the original dataset, multimanifold learning is effective in enhancing the discriminant ability of the processed dataset. In addition, due to the ability of preserving the spatial neighborhood structure information, tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above analysis, a novel tensorbased low rank graph with multi-manifold regularization (TLGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In T-LGMR, a low rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.