Superpixel-guided locality preserving projection and spatial–spectral classification for hyperspectral image

Locality preserving projection (LPP) is a typical feature extraction method based on spectral information for hyperspectral image (HSI) classification. Recently, to improve the classification performance, the spatial information of HSI has been applied in the LPP method. However, for most of spatial–spectral-based LPP methods, they explore the spatial–spectral information within a fixed local window, which cannot be appropriate to the irregular-shape ground objects in HSI. To over this issue, an effective superpixel-guided LPP and spatial–spectral classification method are proposed, in which the spatial–adaptive structure information is fully excavated for HSI classification. Specifically, superpixel segmentation is first conducted on the HSI to generate shape-adaptive homogeneous subregions. Then, to learn more discriminative projection, the neighbourhood graph for LPP is constructed based on spatial–spectral similarity, in which pixels within the same superpixel are connected. Finally, the obtained projection feature is input a classifier to yield the initial classification result, and the edge information of ground objects captured by superpixels is utilized to optimize the initial classification result. Experiments on two real hyperspectral datasets demonstrate that the proposed superpixel-guided and spatial–spectral classification method significantly outperforms the other well-known techniques for HSI classification.