Browsing by Author "Martin, Gabriel"
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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.
- FPGA-based architecture for hyperspectral unmixingPublication . Nascimento, Jose; Véstias, Mário; Martin, GabrielThis paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a Zynq-7020 SoC FPGA based on the Artix-7 FPGA programmable logic and tested using real hyperspectral datasets. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low cost embedded systems.
- GPU implementation of a constrained hyperspectral coded aperture algorithm for compressive sensingPublication . Bernabé, Sérgio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José M.; Plaza, Antonio; Silva, VítorIn this paper, a parallel implementation of a previously constrained hyperspectral coded aperture (CHYCA) algorithm for compressive sensing on graphics processing units (GPUs) is proposed. CHYCA 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 performance of CHYCA relies which does not depend on the tuning of a regularization parameter, which is a time consuming task offering good performance compared with a previously hyperspectral coded aperture (HYCA) method. 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. Experimental results using simulated data reveals speedups up to 56 times, with regards to serial implementation.
- 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 image reconstruction from random projections on GPUPublication . Sevilla, Jorge; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, JoséHyperspectral data compression and dimensionality reduction has received considerable interest in recent years due to the high spectral resolution of these images. Contrarily to the conventional dimensionality reduction schemes, the spectral compressive acquisition method (SpeCA) performs dimensionality reduction based on random projections. The SpeCA methodology has applications in Hyperspectral Compressive Sensing and also in dimensionality reduction. Due to the extremely large volumes of data collected by imaging spectrometers, high performance computing architectures are needed for data compression of high dimensional hyperspectral data under real-time constrained applications. In this paper a parallel implementation of SpeCA on Graphics Processing Units (GPUs) using the compute unified device architecture (CUDA) is proposed. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kemeIs have been optimized to minimize the threads divergence, therefore, achieving high GPU occupancy. The experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 21 tim es, which demonstrates that the GPU implementation can significantly accelerate the methods execution over big datasets while maintaining the methods accuracy.
- On the use of Jetson TX1 board for parallel hyperspectral compressive sensingPublication . Nascimento, Jose; Martin, GabrielHyperspectral imaging instruments measure hundreds of spectral bands (at different wavelength channels) for the same area of the surface of the Earth. Typically the data cube collected by these sensors comprises several GBs per flight, which have attracted attention to on-board techniques for compression. Typically these compression techniques are expensive from the computational point of view. Due to this fact, a number of Compressive Sensing and Random Projection techniques have raised as an alternative to reduce the signal size on-board the sensor. The measuring process of these techniques usually consist on performing dot products between the signal and random vectors. The Compressive Sensing process is performed directly in the optic system, however, in this paper, we propose to perform the random projection measurement process 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 is it possible to obtain real-time performance with a very limited power requirement.
- Parallel hyperspectral coded aperture for compressive sensing on GPUsPublication . Bernabé, Sérgio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José M.; Plaza, António; Silva, VítorThe application of compressive sensing (CS) 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, CS 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 CS, 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: 1) GeForce GTX 590; and 2) 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.
- Parallel hyperspectral compressive sensing method on GPUPublication . Bernabe, Sergio; Martin, Gabriel; Nascimento, JoseRemote 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.
- Parallel hyperspectral image reconstruction using random projectionsPublication . Sevilla, Jorge; Martin, Gabriel; Nascimento, JoseSpaceborne 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.