Compression Of High Spectral Resolution Imagery'

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Abstract
NASA will acquire billions of gigabytes of data over the next decade. Often there is a problem just funneling the data down to earth. The 80 foot long Earth Orbiting Satellite (EOS), scheduled for launch in the mid-1990s, is a prime example. EOS will include a next generation multispectral imaging system (HIRIS) having unprecedented spatial and spectral resolution. Its high resolution, however, comes at the cost of a raw data rate which exceeds the communication channel capacity assigned to the entire EOS mission. This paper explores noisy compression algorithms which may compress multispectral data by up to 30:1 or more. Algorithm performance is measured using both traditional (mse) and mission-oriented criteria (e.g., feature classification consistency). We show that vector quantization, merged with suitable preprocessing techniques, emerges as the most viable candidate.

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