Parallelization of hyperspectral imaging classification and dimensionality reduction algorithms

Hyperspectral imaging provides the capability to identify and classify materials remotely. The applications of such technology is applied everywhere from medical devices and military targets to environmental sciences. With the ongoing advances in spectrometers (spatial resolution and bits per pixel density) the data gathered is constantly increasing. Some hyperspectral imaging algorithms could easily take days or weeks in analyzing a full single hyperspectral data set. In this thesis we performed a porting and parallelization of four hyperspectral algorithms representative of the type of analysis done in a typical data set. Two of the algorithms are in the area of data classification, one in the area of feature reduction and the other one is a combination of both areas. The parallelized algorithms were benchmarked on the Intel 32 bits Pentium M architecture and the new Intel 64 bits Itanium 2 architecture. For three of the four algorithms we demonstrated that the use of parallel approaches in combination with computational clusters speedup significantly the executions times and provide great scalability. On the other algorithm, based on linear algebra manipulations using distributed objects, we obtained execution times that took longer than the sequential implementation. A systematic performance analysis is carried out to explain the performance behavior of the algorithms.