Parallel Hyperspectral Coded Aperture for Compressive Sensing on GPUs
Garcia, S. B. G.
; Martin, G. Martin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 9, Nº 2, pp. 932 - 944, May, 2015.
ISSN (print): 1939-1404
Journal Impact Factor: 3,026 (in 2014)
Digital Object Identifier: 10.1109/JSTARS.2015.2436440
The application of compressive sensing to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, compressive sensing algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient
implementations of hyperspectral coded aperture (HYCA) for compressive sensing, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and
P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets 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. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.