Improving the Scalability of Parallel Algorithms for Hyperspectral Image Analysis Using Adaptive Message Compression

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In previous work, the authors have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values.