Visualization of Hyperspectral Imaging Data Based on Manifold Alignment

Abstract

Tristimulus display of the abundant information contained in a hyper spectral image is a challenging task. Previous visualization approaches focused on preserving as much information as possible in the reduced spectral space, but ended up with displaying hyper spectral images as false color images, which contradicts with human experience and expectation. This paper proposes a new framework to tackle this problem. It is based on the fusion of a hyper spectral image and a high-resolution color image via manifold alignment technique. Manifold learning is an important tool for dimension reduction. Manifold alignment projects a pair of two data sets into a common embedding space so that the pairs of corresponding points in these two data sets are pair wise aligned in this new space. Hyper spectral image and high-resolution color image have strong complementary properties due to the high spectral resolution in the former and the high spatial resolution in the latter. The embedding space produced by manifold alignment bridges a gap between the high dimensional spectral space of hyper spectral image and RGB space of color image, making it possible to transfer the natural color and spatial information of a high-resolution color image to a hyper spectral image to generate a visualized image with natural color distribution and finer details.