Trigueros-Espinosa, Blas

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Gpu-based implementation of target detection algorithms for hyperspectral images using nvidiar cuda
(2011) Trigueros-Espinosa, Blas; Vélez-Reyes, Miguel; College of Engineering; Hunt, Shawn D.; Department of Electrical and Computer Engineering; Castellanos, Dorial
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 GPUs, due to their highly parallel structure and the high computational capabilities that can be achieved at relative low costs. In this work, we studied the parallel implementation of target detection algorithms for hyperspectral images in order to identify the aspects in the structure of these algorithms that can exploit the parallel computing resources of GPUs based on the NVIDIA⃝R CUDATM architecture. A dataset was generated using a SOC-700 hyperspectral imager to evaluate the performance and detection accuracy of the parallel implementations. In addition, a library of target detectors was developed to facilitate the use of the algorithms by future researchers.