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Parallel Processing for Normal Mixture Models of Hyperspectral Data Using a Graphics Processor

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4 Author(s)
Tarabalka, Y. ; Norwegian Defence Res. Establ. (FFI), Kjeller ; Haavardsholm, T.V. ; Kasen, I. ; Skauli, T.

Multivariate normal mixture models, where a complex statistical distribution is represented by a weighted sum of several multivariate normal probability distributions, have many potential applications including anomaly detection (AD) in hyperspectral (HS) images. The high computational cost of mixture models requires hardware and/or algorithmic acceleration to make AD run in real time. In this paper we describe the concurrency present in the AD algorithm that includes a normal mixture estimation task. We explore the use of graphics processing units (GPUs) for parallel implementation of the algorithm. The GPU implementations provide a significant speedup compared to multi-core central processing unit (CPU) implementations, and enable the algorithm to execute in real time.

Published in:

Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International  (Volume:2 )

Date of Conference:

7-11 July 2008