A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing
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Information and Communication Engineering College,Harbin Engineering University,Information and Communication Engineering College,Harbin Engineering University,Information and Communication Engineering College,Harbin Engineering University

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    Abstract:

    Anomaly detection is one of the most important applications in hyperspectral imagery. Real-time processing is the main issue we are facing due to the large data set. Real time causal processing algorithms were developed to perform anomaly detection. It is an innovational kalman filtering based processing by using Woodburys identity to update information which provides the pixel currently being processed without re-processing previous pixels. Experimental results demonstrated the proposed algorithm significantly improves processing efficiency in comparison with conventional anomaly detection without real time causal processing.

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ZHAO Chun-Hui, WANG Yu-Lei, LI Xiao-Hui. A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing[J]. Journal of Infrared and Millimeter Waves,2015,34(1):114~121

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History
  • Received:May 19,2013
  • Revised:August 06,2013
  • Adopted:August 09,2013
  • Online: April 03,2015
  • Published: