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Advisor(s)
Abstract(s)
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which significantly reduces the computing time. A design for the commodity graphics processing units of the two methods is presented and evaluated. Experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 100 times, which grants real-time response required by many remotely sensed hyperspectral applications.
Description
Keywords
Graphics Processing Unit (GPU) Parallel Methods Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUNSAL) Unsupervised Hyperspectral Unmixing, Vertex Component Analysis (VCA)
Citation
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.