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Recent advances in hyperspectral imaging sensors allow the acquisition of images of a scene at hundreds of contiguous narrow spectral bands. Target detection algorithms try to exploit this high-resolution spectral information to detect target materials present in a scene, but this process may be computationally intensive due to the large data volumes generated by the hyperspectral sensors, typically hundreds of megabytes. Previous works have shown that hyperspectral data processing can significantly benefit from the parallel computing resources of graphics processing units (GPUs), due to their highly parallel structure and the high computational capabilities that can be achieved at relative low costs. We studied the parallel implementation of three target detection algorithms (RX algorithm, matched filter, and adaptive matched subspace detector) for hyperspectral images in order to identify the aspects in the structure of these algorithms that can exploit the CUDA™ architecture of NVIDIA® GPUs. A data set was generated using a SOC-700 hyperspectral imager to evaluate the performance and detection accuracy of the parallel implementations on a NVIDIA® Tesla™ C1060 graphics card, achieving real-time performance in the GPU implementations based on global statistics.

Evaluation of the graphics processing unit architecture for the implementation of target detection algorithms for hyperspectral imagery