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Isto amosa as diferenzas entre a revisión seleccionada e a versión actual da páxina.
| Next revision | Previous revision | ||
| hiperespectral:hyfm-gpu [2016/11/21 14:23] – creado alvaro.ordonez | hiperespectral:hyfm-gpu [2017/11/10 10:48] (actual) – [GPU Accelerated FFT-based Registration of Hyperspectral Scenes] alvaro.ordonez | ||
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| Liña 1: | Liña 1: | ||
| ====== GPU Accelerated FFT-based Registration of Hyperspectral Scenes ====== | ====== GPU Accelerated FFT-based Registration of Hyperspectral Scenes ====== | ||
| - | Experimental results related to the paper **GPU Accelerated FFT-based Registration of Hyperspectral Scenes** by Álvaro Ordóñez, Francisco Argüello, and Dora B., that it is under revision. | + | Experimental results related to the paper [[https:// |
| ===== Abstract ===== | ===== Abstract ===== | ||
| - | Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for Nvidia GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to 245.0×. | + | Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for Nvidia GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to 240.6×. |
| ===== Downloads ===== | ===== Downloads ===== | ||
| Liña 10: | Liña 10: | ||
| Compiled program to register two hyperspectral images on GPU. | Compiled program to register two hyperspectral images on GPU. | ||
| - | * HYFM algorithm on GPU: Coming soon. | + | * HYFM algorithm on GPU: {{ : |
| === Images === | === Images === | ||