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Proceedings Paper

Parallel hyperspectral compressive sensing method on GPU
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Paper Abstract

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Paper Details

Date Published: 20 October 2015
PDF: 8 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460P (20 October 2015); doi: 10.1117/12.2194520
Show Author Affiliations
Sergio Bernabé, Instituto de Telecomunicações (Portugal)
Gabriel Martín, Instituto de Telecomunicações (Portugal)
José M. P. Nascimento, Instituto de Telecomunicações (Portugal)

Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang D.D.S.; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

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