Loading...
4 results
Search Results
Now showing 1 - 4 of 4
- 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.
- Parallel method for sparse semisupervised hyperspectral unmixingPublication . Nascimento, Jose; Rodríguez Alves, José M.; Plaza, Antonio; Silva, Vítor; Bioucas-Dias, José M.Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.
- 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.
- Hyperspectral unmixing algorithm via dependent component analysisPublication . Nascimento, Jose; Bioucas-Dias, José M.This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.