Removing shadows from hyper-spectral images

AUGUST 25, 2008--Varying levels of illumination due to shadows and cloud cover, among others, are a known problem for manyhyperspectral segmentation and targeting algorithms. The primary advantage to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no a priori knowledge of the sample and post-processing allows all available information from the data set to be mined. Shadows lower the overall intensity of the radiance spectrum and cause a change in the spectral shape because the color of the light in a shadowed region is different from that in a sunny area. Edward Ashton at VirtualScopics (Rochester, NY; www.virtualscopics.com) is developing a method to identify shadows in a scene and hence appropriately adjust the illumination level and color to better match similar materials in non-shadowed regions.

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