A Comparative Analysis of GPU Implementations of Spectral Unmixing Algorithms

Sergio Sanchez, Antonio Plaza
Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, Avda. de la Universidad s/n, 10071 Caceres, Spain
Proceedings SPIE 8183, 81830E, 2011


   title={A comparative analysis of GPU implementations of spectral unmixing algorithms},

   author={Sanchez, S. and Plaza, A.},

   booktitle={Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},





Download Download (PDF)   View View   Source Source   



Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It involves the separation of a mixed pixel spectrum into its pure component spectra (called endmembers) and the estimation of the proportion (abundance) of each endmember in the pixel. Over the last years, several algorithms have been proposed for: i) automatic extraction of endmembers, and ii) estimation of the abundance of endmembers in each pixel of the hyperspectral image. The latter step usually imposes two constraints in abundance estimation: the non-negativity constraint (meaning that the estimated abundances cannot be negative) and the sum-toone constraint (meaning that the sum of endmember fractional abundances for a given pixel must be unity). These two steps comprise a hyperspectral unmixing chain, which can be very time-consuming (particularly for high-dimensional hyperspectral images). Parallel computing architectures have offered an attractive solution for fast unmixing of hyperspectral data sets, but these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time. In this paper, we perform an inter-comparison of parallel algorithms for automatic extraction of pure spectral signatures or endmembers and for estimation of the abundance of endmembers in each pixel of the scene. The compared techniques are implemented in graphics processing units (GPUs). These hardware accelerators can bridge the gap towards on-board processing of this kind of data. The considered algorithms comprise the orthogonal subspace projection (OSP), iterative error analysis (IEA) and N-FINDR algorithms for endmember extraction, as well as unconstrained, partially constrained and fully constrained abundance estimation. The considered implementations are inter-compared using different GPU architectures and hyperspectral data sets collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2023 hgpu.org

All rights belong to the respective authors

Contact us: