Visualization of Hyperspectral Images Using Moving Least Squares

Displaying the large number of bands in a hyperspectral image (HSI) on a trichromatic monitor has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate image interpretation. Most existing methods display HSIs in false colors, which contradict with human’s experience and expectation. In this paper, we propose a nonlinear approach to visualize an input HSI with natural colors by taking advantage of a corresponding RGB image. Our approach is based on Moving Least Squares (MLS), an interpolation scheme for reconstructing a surface from a set of control points, which in our case is a set of matching pixels between the HSI and the corresponding RGB image. Based on MLS, the proposed method solves for each spectral signature a unique transformation so that the nonlinear structure of the HSI can be preserved. The matching pixels between a pair of HSI and RGB image can be reused to display other HSIs captured by the same imaging sensor with natural colors. Experiments show that the output images of the proposed method not only have natural colors but also maintain the visual information necessary for human analysis.