Compressive Spectral Imaging for Accurate Remote Sensing by John Greer, Justin Christopher Flake, Maria Busuioceanu and David W. Messinger

Abstract: Hyperspectral imaging (HSI) involves collecting and processing information to generate a spatial map of spectral variation. It records tens or hundreds of images from closely spaced wavelengths, or spectral bands, as opposed to just red, green, and blue, as in standard color imaging. It plays an important role in remote sensing tasks such as land-cover classification, and target and anomaly detection. However, traditional hyperspectral cameras, or sensors, are expensive, data-heavy, and slow. They build what are known as 3D hyperspectral data cubes out of information collected as a set of 2D images that are often very similar to each other. The time taken to collect this data makes it a costly process. It also leads to image distortion due to motion from the platform, such as a satellite or airplane, or in-scene objects, for example, moving vehicles.

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