24 October 2016 OpenCL-library-based implementation of SCLSU algorithm for remotely sensed hyperspectral data exploitation: clMAGMA versus viennaCL
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Abstract
In the last decade, hyperspectral spectral unmixing (HSU) analysis have been applied in many remote sensing applications. For this process, the linear mixture model (LMM) has been the most popular tool used to find pure spectral constituents or endmembers and their fractional abundance in each pixel of the data set. The unmixing process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be very expensive computationally, a fact that compromises their use in applications under real-time constraints. This is, mainly, due to the last two stages in the unmixing process, which are the most consuming ones. In this work, we propose parallel opencl-library- based implementations of the sum-to-one constrained least squares unmixing (P-SCLSU) algorithm to estimate the per-pixel fractional abundances by using mathematical libraries such as clMAGMA or ViennaCL. To the best of our knowledge, this kind of analysis using OpenCL libraries have not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to achieve efficient implementations using parallel routines. The efficacy of our proposed implementations is demonstrated through Monte Carlo simulations for real data experiments and using high performance computing (HPC) platforms such as commodity graphics processing units (GPUs).
Conference Presentation
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Sergio Bernabé , Guillermo Botella, Carlos Orueta, José M. R. Navarro, Manuel Prieto-Matías, Antonio Plaza, "OpenCL-library-based implementation of SCLSU algorithm for remotely sensed hyperspectral data exploitation: clMAGMA versus viennaCL", Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 100070B (24 October 2016); doi: 10.1117/12.2241524; https://doi.org/10.1117/12.2241524
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