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Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

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. In many surveillance applications, the size of the objects (targets) searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over theWorld Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

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Correspondence to Antonio Plaza.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paz, A., Plaza, A. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images. EURASIP J. Adv. Signal Process. 2010, 915639 (2010). https://doi.org/10.1155/2010/915639

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Keywords

  • Terrorist Attack
  • Anomaly Detection
  • Hyperspectral Image
  • Hyperspectral Data
  • Surveillance Application
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