Efficient implementations of hyperspectral chemical-detection algorithms

Many military and civilian applications depend on the ability to remotely sense chemical clouds using hyperspectral imagers, from detecting small but lethal concentrations of chemical warfare agents to mapping plumes in the aftermath of natural disasters. Real-time operation is critical in these applications but becomes diffcult to achieve as the number of chemicals we search for increases. In this paper, we present efficient CPU and GPU implementations of matched-filter based algorithms so that real-time operation can be maintained with higher chemical-signature counts. The optimized C++ implementations show between 3x and 9x speedup over vectorized MATLAB implementations.