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Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters

Molero, J. M. ; Paz, A. ; Garzón, E. M. ; Martínez, J. A. ; Plaza, A. ; García, I.

Journal of supercomputing, 2011-12, Vol.58 (3), p.411-419 [Periódico revisado por pares]

Boston: Springer US

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Título:
Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters
Autor: Molero, J. M. ; Paz, A. ; Garzón, E. M. ; Martínez, J. A. ; Plaza, A. ; García, I.
Assuntos: Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Artificial intelligence ; Compilers ; Computer Science ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Interpreters ; Memory organisation. Data processing ; Pattern recognition. Digital image processing. Computational geometry ; Processor Architectures ; Programming Languages ; Software ; Theoretical computing
É parte de: Journal of supercomputing, 2011-12, Vol.58 (3), p.411-419
Descrição: 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.
Editor: Boston: Springer US
Idioma: Inglês

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Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters

Molero, J. M. ; Paz, A. ; Garzón, E. M. ; Martínez, J. A. ; Plaza, A. ; García, I.

Boston: Springer US

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