Research on Anomaly Detection Based on MNF in Hyper Spectral Imagery

Guang Yang; Zhong Xiang Lei; He Nan Wu; Qiang Qiang Meng; Gao Pan He

doi:10.4028/www.scientific.net/AMM.644-650.1085

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Research on Anomaly Detection Based on MNF in...

Research on Anomaly Detection Based on MNF in Hyper Spectral Imagery

Abstract:

A novel method was proposed to solve the problem which was caused by high dimensions. Minimum Noise Fraction was used for dimension reduction. And then the RX algorithm and KRX algorithm was used to detect the data after dimensional reduction. The method proposed was better by comparing the ROC of four detection results.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

1085-1088

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.1085

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Online since:

September 2014

Authors:

Guang Yang*, Zhong Xiang Lei, He Nan Wu, Qiang Qiang Meng, Gao Pan He

Keywords:

Anomaly Detection, Hyper Spectral Data, Minimum Noise Fraction

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* - Corresponding Author

References

[1] Vidal M, Amigo J M. Pre-processing of Hyper spectral Images[J]. Chemometrics and Intelligent Laboratory Systems, 2012, 34(2): 274-286.

Google Scholar

[2] Benediktsson JA, Swain P H. Consensus Theoretic Classification Methods. IEEE Transactions on System Man and Cybernetics, 1992, 22(4): 688-704.

DOI: 10.1109/21.156582

Google Scholar

[3] Chen J Y, Reed I S. A Detection Algorithm for Opital Targets in Clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, 23(1): 46-59.

DOI: 10.1109/taes.1987.313335

Google Scholar

[4] Reed I S, Yu X L. Adaptive Multiple-Band CFAR Detection of An Optical Pattern with Unknown Spectral Distribution. IEEE Transactions on Acoustics, Speech and Signal Processing[J], 1990, 38(10): 1760-1770.

DOI: 10.1109/29.60107

Google Scholar

[5] Chang C I. Hyper spectral Imaging: Techniques for Spectral Detection and Classification[M]. New York: Kluwer Academic, (2003).

Google Scholar

[6] Richards J A., Jia X. Remote Sensing Digital Image Processing[M]. New York: Springer-Verlag, (1993).

Google Scholar

[7] Harsanyi, Charles J. Detection and Classification of Subpixel Spectral Signatures in Hyper spectral Image Sequences[D]. USA: University of Maryland Baltimore County, 1993: 56-100.

Google Scholar

[8] Hsueh M, Chang C I. Adaptive Causal Anomaly Detection for Hyper spectral Imagery[C]. IEEE International on Geoscience and Remote Sensing Symposium, 2004, 5: 3222-3224.

DOI: 10.1109/igarss.2004.1370387

Google Scholar

[9] Hsuan R. Weighted Anomaly Detection for Hyper spectral Remotely Sensed Images[J]. SPIE, 2005, 5995(1): 20-27.

Google Scholar

[10] Manolakis D, Siracusa C, Mardcn D. Hyper spectral Adaptive Matched Filter Detectors: Practical Performance Comparison[J]. SPIE, 2001, 4831: 18-33, 40-42.

Google Scholar