Aggregation of parallel computing and hardware/software co-design techniques for high-performance remote sensing applications

Developing computationally efficient processing techniques for massive volumes of hyperspectral data is critical for space-based Earth science and planetary exploration. In particular, many remote sensing imaging applications require a response in real time in areas such as environmental modeling and assessment, target detection for military and homeland defense/security purposes, and risk prevention and response. This paper propose the aggregation of parallel computing and HW/SW co-design techniques using processor arrays (PAs) units as specialized hardware architectures for the real time enhancement of remote sensing imagery. An extended descriptive experiment design regularization (DEDR) method that incorporates projections onto convex solution sets (POCS) for spatial spectrum pattern (SSP) reconstruction is used to be efficiently implemented (i.e., HW-level) via the new proposition of the aggregation techniques. Finally, it is reported and discussed the Xilinx Virtex-5 FPGA implementation and high-performance issues related to real time enhancement of large-scale real-world RS imagery.