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- A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCLPublication . Bernabé, Sergio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José M.; Plaza, Antonio; Botella, Guillermo; Prieto-Matias, ManuelIn this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.
- GPU implementation of a hyperspectral coded aperture algorithm for compressive sensingPublication . Bernabe, Sergio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José; Plaza, Antonio; Silva, VítorThis paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.
- Hyperspectral compressive sensing on low energy consumption boardPublication . Nascimento, Jose; Martin, GabrielHyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands (at different wavelength channels) for the same area of the Earth surface. The acquired data cube comprises several GBs per flight, which have attracted attention to onboard compression techniques. Typically these compression techniques are expensive from the computational point of view. This paper presents a compressive sensing method implementation on a low power consumption Graphic Processing Unit. The experiments are conducted on a Jetson TX1 board, which is well suited to perform vector operations such as dot products. These experiments have been performed to demonstrate the applicability, in terms of accuracy and time consuming, of these methods for onboard processing. The results show that by using this low power consumption GPU it is possible to obtain real-time performance with a very limited power requirements.