Commodity Cluster-Based Parallel Implementation of an Automatic Target Generation Process for Hyperspectral Image Analysis
The development of parallel implementations is an important task for hyper spectral data exploitation. In most cases, real-time or nearly real-time processing of hyper spectral images is required for swift decisions which depend upon high computing performance of algorithm analysis. A popular algorithm in hyper spectral image interpretation is the automatic target generation process (ATGP). In this paper, we develop a new parallel version of this algorithm, which is routinely applied in many application domains, including defence and intelligence, precision agriculture, geology, or forestry. We improve considerably the computational cost of this algorithm, and also improve its detection accuracy by incorporating a new method for calculating the orthogonal projection process in which the algorithm is based using the Gram-Schmidt method. Our proposed strategy reduces the computational cost over the a previous implementation of the same algorithm which uses the pseudoinverse operation to compute the orthogonal projection. Our parallel algorithm is implemented on a multi-core cluster system made up of of sixteen nodes, with two CPUs of four cores per node, and quantitatively evaluated using hyper spectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York and over the Cup rite mining district, Nevada, United States.