Visualization of hyperspectral imagery based on manifold learning

Displaying the abundant information contained in a hyperspectral image is a challenging task. Previous visualization approach focused only on preserving the structure in the original images. They ended up with presenting pseudo-color images and stopped short of adjusting the color of the images to retrieve more desirable visual effects. In this paper, a new visualization algorithm is proposed. It can be modeled as a two stage approach. At the first stage, Laplacian Eigenmaps algorithm is applied to reduce the dimension of the hyperspectral image. In this way we obtain a three dimensional image with pseudo-color. At the second stage, we transfer the natural color of a panchromatic image to the image obtained by the first step via manifold alignment. Experimental results show that the visualized image not only retains the structure of the hyperspectral image but also possesses natural colors.