Dimensionality Reduction of Hyperspectral Image Using Spatial Regularized Local Graph Discriminant Embedding

Dimensionality reduction (DR) is an important preprocessing step for hyperspectral image (HSI) classification. Recently, graph-based DR methods have been widely used. Among various graph-based models, the local graph discriminant embedding (LGDE) model has shown its effectiveness due to the complete use of label information. Besides spectral information, an HSI also contains rich spatial information. In this paper, we propose a regularization method to incorporate the spatial information into the LGDE model. Specifically, an oversegmentation method is first employed to divide the original HSI into nonoverlapping superpixels. Then, based on the observation that pixels in a superpixel often belong to the same class, intraclass graphs are constructed to describe such spatial information. Finally, the constructed superpixel-level intraclass graphs are used as a regularization term, which can be naturally incorporated into the LGDE model. Besides, to sufficiently capture the nonlinear property of an HSI, the linear LGDE model is further extended into its kernel counterpart. To demonstrate the effectiveness of the proposed method, experiments have been established on three widely used HSIs acquired by different hyperspectral sensors. The obtained results show that the proposed method can achieve higher classification performance than many state-of-the-art graph embedding models, and the kernel extension model can further improve the classification performance.