Parallel hyperspectral image reconstruction using random projections

Jorge Sevilla; Gabriel Martín; José M. P. Nascimento

doi:10.1117/12.2241252

24 October 2016 Parallel hyperspectral image reconstruction using random projections

Jorge Sevilla, Gabriel Martín, José M. P. Nascimento

Proceedings Volume 10007, High-Performance Computing in Geoscience and Remote Sensing VI; 1000707 (2016) https://doi.org/10.1117/12.2241252
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA).

Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.

Conference Presentation

Citation Download Citation

Jorge Sevilla, Gabriel Martín, and José M. P. Nascimento "Parallel hyperspectral image reconstruction using random projections", Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 1000707 (24 October 2016); https://doi.org/10.1117/12.2241252

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