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Remote Sens. 2015, 7(4), 3966-3985; doi:10.3390/rs70403966

Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 23 October 2014 / Revised: 18 March 2015 / Accepted: 18 March 2015 / Published: 1 April 2015

Abstract

Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly detection is the RX detector. A number of variations have been studied since then, including global and local versions for different type of anomalies. Aiming at a real-time requirement for practical applications, this paper extends the concept of global and local anomaly detectors to be real-time detectors. The algorithms exploit the fact that a true real-time detector must produce its output in a causal manner and at the same time as an input comes in. Taking advantage of the Woodbury matrix identity, the global and local real-time detectors can be implemented and processed pixel-by-pixel in real time. Both synthetic and real hyperspectral imagery are conducted to demonstrate their performance. View Full-Text
Keywords: hyperspectral remote sensing; anomaly detection; real-time; Woodbury matrix identity; sliding local window hyperspectral remote sensing; anomaly detection; real-time; Woodbury matrix identity; sliding local window
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhao, C.; Wang, Y.; Qi, B.; Wang, J. Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery. Remote Sens. 2015, 7, 3966-3985.

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