Improving the scalability of parallel algorithms for hyperspectral image analysis using adaptive message compression

In previous work, we 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. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.