Special Section on High-Performance Computing in Applied Remote Sensing: Part 2

Fast supervised hyperspectral band selection using graphics processing unit

[+] Author Affiliations
Wei Wei, Qian Du, Nicolas H. Younan

Mississippi State University, Department of Electrical and Computer Engineering Mississippi State, Mississippi 39762

J. Appl. Remote Sens. 6(1), 061504 (May 04, 2012). doi:10.1117/1.JRS.6.061504
History: Received September 1, 2011; Revised February 14, 2012; Accepted March 15, 2012
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Abstract.  Band selection is a common technique to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, the reduction process can be achieved by selecting the bands that contain the most object information. It is expected that these selected bands can offer an overall satisfactory detection and classification performance. In this paper, we propose a new particle swarm optimization (PSO) based supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, this method requires much less computing time than other traditional methods. However, the PSO process itself may introduce additional computation cost. To tackle this problem, we propose parallel implementations via emerging general-purpose graphics processing units that can provide satisfactory results in speedup when compared to the cluster-based parallel implementation.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Wei Wei ; Qian Du and Nicolas H. Younan
"Fast supervised hyperspectral band selection using graphics processing unit", J. Appl. Remote Sens. 6(1), 061504 (May 04, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.061504


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