Abstract
Anomaly detection using sliding windows is not new but using causal sliding windows has not been explored in the past. The need for causality arises from real-time processing where the used sliding windows should not include future data samples that have not been visited, i.e., those data sample vectors come in after the currently being processed data sample vector. This chapter presents an approach developed by Chang et al. (2015) to anomaly detection using causal sliding windows, which has the capability of being implemented in real time. In doing so, two types of causal windows are defined, causal sliding matrix windows including square matrix windows and rectangular matrix windows and causal sliding array windows, each of which derives a causal sample covariance/correlation matrix for causal anomaly detection. As for the causal sliding array windows, recursive update equations are also derived and, thus, can speed up real-time processing.
Keywords
- Receive Operating Characteristic
- Receive Operating Characteristic Curve
- Anomaly Detection
- Anomaly Detection Algorithm
- Area Under Curve
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chang, C.-I 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification.New York:Kluwer Academic/Plenum Publishers.
Chang, C.-I 2010. Multiple-parameter receiver operating characteristic analysis for signal detection and classification. IEEE Sensors Journal 10(3):423–442. (invited paper).
Chang, C.-I 2013. Hyperspectral Data Processing: Algorithm Design and Analysis. New Jersey: Wiley.
Chang, C.-I, and M. Hsueh. 2006. Characterization of anomaly detection for hyperspectral imagery. Sensor Review 26(2):137–146.
Chang, C.-I, and H. Ren. 2000. An experiment-based quantitative and comparative analysis of hyperspectral target detection and image classification algorithms. IEEE Transactions on Geoscience and Remote Sensing 38(2):1044–1063.
Chang, C.-I, Y. Wang and S.Y. Chen. 2015. Anomaly detection using causal sliding windows. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 8(7):3260–3270.
Chen, S.-Y., Y. Wang, C.C. Wu, C. Liu, and C.-I Chang. 2014a. Real time causal processing of anomaly detection in hyperspectral imagery. IEEE Transactions on Aerospace and Electronics Systems 50(2):1511–1534.
Chen, S.Y., D. Paylor, and C.-I Chang. 2014b. Anomaly discrimination in hyperspectral imagery. In Satellite data compression, communication and processing X (ST146), SPIE international symposium on SPIE sensing technology + applications, Baltimore, MD, May 5–9, 2014.
Chen, S.Y., Y.C. Ouyang, and C.-I Chang. 2014c. Recursive unsupervised fully constrained least squares methods. In 2014 IEEE international geoscience and remote sensing symposium (IGARSS), Quebec Canada, July 13–18, 2014.
Gonzalez, R.C. and R.E. Woods. 2008. Digital Image Processing, 3rd ed. N.J: Prentice-Hall.
Poor, H.V. 1994. An Introduction to Detection and Estimation Theory, 2nd ed. New York: Springer.
Reed, I.S., and X. Yu. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustic, Speech and Signal Process 38(10):1760–1770.
Wang, Y., S.Y. Chen, C. Liu, and C.-I Chang. 2014a. Background suppression issues in anomaly detection for hyperspectral imagery. In Satellite data compression, communication and processing X (ST146), SPIE international symposium on SPIE sensing technology + applications, Baltimore, MD, May 5–9, 2014.
Wang, Y., C.H. Zhao, and C.-I Chang. 2014b. Anomaly detection using sliding causal windows. In 2014 IEEE International geoscience and remote sensing symposium (IGARSS), Quebec Canada, July 13–18, 2014.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Chang, CI. (2016). Anomaly Detection Using Causal Sliding Windows. In: Real-Time Progressive Hyperspectral Image Processing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6187-7_18
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6187-7_18
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-6186-0
Online ISBN: 978-1-4419-6187-7
eBook Packages: EngineeringEngineering (R0)