Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters |
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Authors: | J M Molero A Paz E M Garzón J A Martínez A Plaza I García |
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Affiliation: | (1) Norwegian Defence Research Establishment (FFI), P.O. Box 25, 2007 Kjeller, Norway |
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Abstract: | Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral
domain, and this information can be used to address detection tasks in many applications. One of the most widely used and
successful algorithms for anomaly detection in hyperspectral images is the RX algorithm. Despite its wide acceptance and high
computational complexity when applied to real hyperspectral scenes, few approaches have been developed for parallel implementation
of this algorithm. In this paper, we evaluate the suitability of using a hybrid parallel implementation with a high-dimensional
hyperspectral scene. A general strategy to automatically map parallel hybrid anomaly detection algorithms for hyperspectral
image analysis has been developed. Parallel RX has been tested on an heterogeneous cluster using this routine. The considered
approach is quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer
system over the World Trade Center in New York, 5 days after the terrorist attacks. The numerical effectiveness of the algorithms
is evaluated by means of their capacity to automatically detect the thermal hot spot of fires (anomalies). The speedups achieved
show that a cluster of multi-core nodes can highly accelerate the RX algorithm. |
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