Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/6089
Título: Parallel hyperspectral compressive sensing method on GPU
Autor: Bernabe, Sergio
Martin, Gabriel
Nascimento, José M. P.
Palavras-chave: Hyperspectral compressive sensing
High performance computing
Graphics Processing Units
Data: 2015
Editora: SPIE-Institute Societe Optical Engineering
Citação: BERNABE, Sergio; [et al] - Parallel hyperspectral compressive sensing method on GPU. Conference on High-Performance Computing in Remote Sensing V. ISSN 0277-786X. 2015
Relatório da Série N.º: Proceedings of SPIE;96460P
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.21/6089
ISBN: 978-1-62841-856-9
ISSN: 0277-786X
Aparece nas colecções:ISEL - Eng. Elect. Tel. Comp. - Comunicações

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