Next Article in Journal
The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
Previous Article in Journal
Telecouplings in the East–West Economic Corridor within Borders and Across
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(12), 1011; doi:10.3390/rs8121011

Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2
Space Science and Engineering Center, University of Wisconsin, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 29 September 2016 / Revised: 29 November 2016 / Accepted: 8 December 2016 / Published: 11 December 2016
View Full-Text   |   Download PDF [9748 KB, uploaded 14 December 2016]   |  

Abstract

Abstract: Real-time anomaly detection has received wide attention in remote sensing image processing because many moving targets must be detected on a timely basis. A widely-used anomaly detection algorithm is the Reed-Xiaoli (RX) algorithm that was proposed by Reed and Yu. The kernel RX algorithm proposed by Kwon and Nasrabadi is a nonlinear version of the RX algorithm and outperforms the RX algorithm in terms of detection accuracy. However, the kernel RX algorithm is computationally more expensive. This paper presents a novel real-time anomaly detection framework based on the kernel RX algorithm. In the kernel RX detector, the inverse covariance matrix and the estimated mean of the background data in the kernel space are non-causal and computationally inefficient. In this work, a local causal sliding array window is used to ensure the causality of the detection system. Using the matrix inversion lemma and the Woodbury matrix identity, both the inverse covariance matrix and estimated mean can be recursively derived without extensive repetitive calculations, and, therefore, the real-time kernel RX detector can be implemented and processed pixel-by-pixel in real time. To substantiate its effectiveness and utility in real-time anomaly detection, real hyperspectral data sets are utilized for experiments. View Full-Text
Keywords: Keywords: hyperspectral remote sensing; real-time; kernel anomaly detection; matrix inversion lemma; Woodbury matrix identity; local causal sliding array window Keywords: hyperspectral remote sensing; real-time; kernel anomaly detection; matrix inversion lemma; Woodbury matrix identity; local causal sliding array window
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhao, C.; Yao, X.; Huang, B. Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm. Remote Sens. 2016, 8, 1011.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top