Abstract
The use of the Karhunen-Loève Transform (KLT) for spectral decorrelation in compression of hyperspectral satellite images results in improved performance. However, the KLT algorithm consists of sequential processes, which are computationally intensive, such as the Covariance and Eigenvector evaluations, etc. These processes slow down the overall computation of the KLT transform significantly. The acceleration of these processes within the context of limited power and hardware budgets is the main objective of this paper. The computations of each of these processes are investigated thoroughly by breaking them down into primitive arithmetic operations. Subsequently, a comprehensive analysis of these computations is presented to inspect the possibility and feasibility of different acceleration techniques, such as parallelism. The proposed designs are implemented on a System-on-a-Chip platform, which incorporates a 32-bit hardwired microcontroller and a coprocessing unit built within a field programmable gate array fabric. Two novel architectures are proposed offering accelerated processing within a very limited power budget (less than 0.225 Watt). The proposed solution is not only feasible for space applications, but also for different mobile and remote sensing applications. © 2012 IEEE.