Novel similarity measure-based nonlinear dimensionality reduction methods for hyperspectral imgery

This paper proposes a new similarity measure to integrate the spectral and spatial-contextual information in the hyperspectral imagery into the manifold learning methods. Including spatial information using the spatial neighbor, the proposed similarity measure is based on the fact that the observation pixels in the hyperspectral imagery are spatially related and relevant information can be extracted from both the spectral and spatial domains. The proposed nonlinear dimensionality reduction techniques based on the new similarity measure can effectively deal with the nonlinearity in the real hyperspectral data as well as the spatial relation among pixels, leading to a more meaningful and manageable representation of original high-dimensional data set with reduced dimensionality. The results from the real hyperspectral image experiments denote that the proposed algorithms significantly increase the classification accuracy for the hyperspectral images compared with other spectral based dimensionality reduction methods.