  Displaying the large number of bands in a hyper- spectral image (HSI) on a
trichromatic monitor is important for HSI processing and analysis system. The
visualized image shall convey as much information as possible from the original
HSI and meanwhile facilitate image interpretation. However, most existing
methods display HSIs in false color, which contradicts with user experience and
expectation. In this paper, we propose a visualization approach based on
constrained manifold learning, whose goal is to learn a visualized image that
not only preserves the manifold structure of the HSI but also has natural
colors. Manifold learning preserves the image structure by forcing pixels with
similar signatures to be displayed with similar colors. A composite kernel is
applied in manifold learning to incorporate both the spatial and spectral
information of HSI in the embedded space. The colors of the output image are
constrained by a corresponding natural-looking RGB image, which can either be
generated from the HSI itself (e.g., band selection from the visible
wavelength) or be captured by a separate device. Our method can be done at
instance-level and feature-level. Instance-level learning directly obtains the
RGB coordinates for the pixels in the HSI while feature-level learning learns
an explicit mapping function from the high dimensional spectral space to the
RGB space. Experimental results demonstrate the advantage of the proposed
method in information preservation and natural color visualization.
