Spatial-spectral graph-based nonlinear embedding dimensionality reduction for hyperspectral image classificaiton

Dimensionality reduction (DR) is one of the most important tasks to improve the performance of hyperspectral images classification. Recently, a sparse and low-rank graph embedding based method (SLGE) has been proposed to describe the intrinsic structure of data combined with the local and global constraint simultaneously, which is effective to reduce the dimension of hyperspectral data and obtain a better classification accuracy. However, SLGE is based on an assumption that low-dimensional feature can be obtained utilizing a linear projection. Its performance may degrade under nonlinearly distributed data. Moreover, spatial prior of HSI is not considered in the framework. In this paper, we proposed a novel dimensionality reduction method named spatial-spectral graph-based non-linear embedding (SSGNE). To generate a new graph-trained data, the segmentation strategy based on superpixel is adopted. The spatial-spectral graph is constructed by constraining the sparsity and low-rankness simultaneously on graph-trained data set. Finally, the kernel trick is adopted to extend the general graph embedding framework to nonlinearly space, which fully considers the complexity of real data. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of the classification accuracy.