Robust Patch Tensor-based Multigraph Embedding for Dimensionality Reduction of Hyperspectral Images.

Since hyperspectral image (HSI) is naturally presented as 3D data cube, patch tensor-based graph embedding methods have been widely applied for dimensionality reduction (DR) of HSI. However, these methods are usually developed with the patch tensors generated by the raw data which is inevitably contaminated by noise. To eliminate the negative affects of noise, this paper introduces region covariance descriptor (RCD) to characterize the HSI data and proposes a robust patch tensor-based multigraph embedding (RPTMGE) method for DR of HSI. RPTMGE constructs three types of subgraphs to comprehensively describe the intrinsic structure of HSI. Specifically, the manifold subgraph in RPTMGE is constructed with the RCD of HSI, which can significantly enhance the robustness of RPTMGE. Finally, experiments on real HSI data are conducted and the results demonstrated the effectiveness of the proposed method.