Introduction to Parallel Computing
Introduction to Parallel Computing
MultiSpec: a tool for multispectral--hyperspectral image data analysis
Computers & Geosciences
Using simulated annealing to obtain optimal linear end-member mixtures of hyperspectral data
Computers & Geosciences
Load balancing across a highly heterogeneous processor cluster using file status probes
Computers & Geosciences
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
Mapping and Load-Balancing Iterative Computations
IEEE Transactions on Parallel and Distributed Systems
Hyperspectral Data Compression
Hyperspectral Data Compression
Journal of Parallel and Distributed Computing
High Performance Computing in Remote Sensing
High Performance Computing in Remote Sensing
Concurrency and Computation: Practice & Experience
High-performance land surface modeling with a Linux cluster
Computers & Geosciences
Solving linear-quadratic optimal control problems on parallel computers
Optimization Methods & Software
Parallel processing of Prestack Kirchhoff Time Migration on a PC Cluster
Computers & Geosciences
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Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are 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 wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.