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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.
- Low power compressive sensing for hyperspectral imageryPublication . Nascimento, Jose; Véstias, MárioHyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.
- 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.
- Hyperspectral compressive sensing with a system-on-chip FPGAPublication . Nascimento, Jose; Véstias, Mário; Martín, GabrielAdvances in hyperspectral sensors have led to a significantly increased capability for high-quality data. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and transmitted to the ground station. An important approach to deal with massive volumes of information is an emerging technique, called compressive sensing, which acquires directly the compressed signal instead of acquiring the full dataset. Thus, reducing the amount of data that needs to be measured, transmitted, and stored in first place. In this article, a hardware/software implementation in a system-on-chip (SoC) field-programmable gate array (FPGA) for compressive sensing is proposed. The proposed hardware/software architecture runs the compressive sensing algorithm with a unitary compression rate over an airborne visible/infrared imaging spectrometer sensor image with 512 lines, 614 samples, and 224 bands in 0.35 s. The proposed system runs 49× and 216× faster than na embedded 256-cores GPU of a Jetson TX2 board and the ARM of the SoC FPGA, respectively. In terms of energy, the proposed architecture requires around 100× less energy.