Local detail enhanced hyperspectral image visualization

A new method for hyperspectral image (HSI) visualization is proposed in this paper, which emphasizes on pairwise distance preservation and detail enhancement. It includes two sequential steps. The first reduces the high dimensional spectral space of HSI to 3-D color space by distance preservation method, and then enhancesthe detailed information by Laplacian pyramid. Distance preservation is an optimization problem that minimizes the difference between the pairwise pixel distances in the original spectral space and the corresponding color space. In general, solving this optimization problem is always very time and storage consuming. A multi-resolution multidimensional scaling algorithm is proposed in this paper to mitigate this hardness. Obviously the loss of some local details is not avoided in multidimensional scaling. In order to enhance the spatial distinction of different scene objects, Laplacian pyramid is used to draw the locally details from the original HSI, and embed it into the color image. The proposed HSI visualization method takes the global and local information of spectral and spatial distribution in HSI into account for visualization, which makes the color display of HSI carry as much original information as possible.