NUFFT- & GPU-Based Fast Imaging of Vegetation

Amedeo CAPOZZOLI  Claudio CURCIO  Antonio DI VICO  Angelo LISENO  

IEICE TRANSACTIONS on Communications   Vol.E94-B   No.7   pp.2092-2103
Publication Date: 2011/07/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E94.B.2092
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Sensing
imaging of vegetation,  Non-Uniform FFT (NUFFT),  Graphics Processing Unit (GPU),  parallel processing,  Filtered Backprojection (FBP),  radon transform inversion,  CUDA,  polarimetric,  temporal decorrelation,  indoor setup,  

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We develop an effective algorithm, based on the filtered backprojection (FBP) approach, for the imaging of vegetation. Under the FBP scheme, the reconstruction amounts at a non-trivial Fourier inversion, since the data are Fourier samples arranged on a non-Cartesian grid. The computational issue is efficiently tackled by Non-Uniform Fast Fourier Transforms (NUFFTs), whose complexity grows asymptotically as that of a standard FFT. Furthermore, significant speed-ups, as compared to fast CPU implementations, are obtained by a parallel versions of the NUFFT algorithm, purposely designed to be run on Graphic Processing Units (GPUs) by using the CUDA language. The performance of the parallel algorithm has been assessed in comparison to a CPU-multicore accelerated, Matlab implementation of the same routine, to other CPU-multicore accelerated implementations based on standard FFT and employing linear, cubic, spline and sinc interpolations and to a different, parallel algorithm exploiting a parallel linear interpolation stage. The proposed approach has resulted the most computationally convenient. Furthermore, an indoor, polarimetric experimental setup is developed, capable to isolate and introduce, one at a time, different non-idealities of a real acquisition, as the sources (wind, rain) of temporal decorrelation. Experimental far-field polarimetric measurements on a thuja plicata (western redcedar) tree point out the performance of the set up algorithm, its robustness against data truncation and temporal decorrelation as well as the possibility of discriminating scatterers with different features within the investigated scene.