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Proceedings Paper

Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images
Author(s): Abel Paz; Antonio Plaza; Javier Plaza
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Paper Abstract

Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.

Paper Details

Date Published: 31 August 2009
PDF: 11 pages
Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550X (31 August 2009); doi: 10.1117/12.825458
Show Author Affiliations
Abel Paz, Univ. of Extremadura (Spain)
Antonio Plaza, Univ. of Extremadura (Spain)
Javier Plaza, Univ. of Extremadura (Spain)

Published in SPIE Proceedings Vol. 7455:
Satellite Data Compression, Communication, and Processing V
Bormin Huang; Antonio J. Plaza; Raffaele Vitulli, Editor(s)

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