Anomaly detection for hyperspectral imaginary
A.Yu. Denisova, V.V. Myasnikov

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Full text of article: Russian language.

DOI: 10.18287/0134-2452-2014-38-2-287-296

Pages: 287-296.

Abstract:
In this paper authors offered several algorithms for anomaly detection on hyperspectral images. Algorithms used different ideas to describe anomalies. A comparison between offered in article algorithms and RXD-detector was provided. An advances of proposed solutions were overviewed.

Key words:
hyperspectral images, anomaly detection, spectral mismatch, RX anomaly detector.

References:

  1. Chandola, V. Anomaly detection: A survey / V. Chandola, A. Banerjee, V. Kumar // ACM Computing Surveys (CSUR). – 2009. – V. 41(3). – 72 p.
  2. Chang, C.I. Anomaly detection and classification for hyperspectral imagery / C.I. Chang, C.S hao-Shan // IEEE Transactions on Geoscience and Remote Sensing. – 2002. – V. 40(6). – P. 1314-1325.
  3. Chang, C.I. Hyperspectral Data Processing: Algorithm Design and Analysis / C.I. Chang. – John Wiley & Sons, 2013. – 1164 p.
  4. Chang, C.I. Hyperspectral data exploitation: theory and applications / C.I. Chang. – Wiley-Interscience, 2007. – 456 p.
  5. Reed, I.S. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution / I.S. Reed, X. Yu // IEEE Transactions on Acoustics, Speech, and Signal Processing. – V. 38(10). – 1990. – P. 1760-1770.
  6. Matteoli, S. A tutorial overview of anomaly detection in hyperspectral images/ S. Matteoli, M. Diani, G. Corsini // Aerospace and Electronic Systems Magazine, IEEE. – 2010. – V. 25(7). – P. 5-28.
  7. Borghys, D. Hyperspectral anomaly detection: A comparative evaluation of methods / D. Borghys, V. Achard, S.R. Rotman, N. Gorelik, C. Perneel, E. Schweicher // General Assembly and Scientific Symposium, 2011 XXXth URSI. – 2011. – P. 1-4.
  8. Borghys, D. Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity / D. Borghys, I. Kasen, V. Achard, C. Pernee // Journal of Electrical and Computer Engineering. – 2012. – V. 2012. – 16 p. – Article ID 162106.
  9. Bachega, L.R., Evaluating and improving local hyperspectral anomaly detectors / L.R. Bachega, J. Theiler, C.A. Bouman // Applied Imagery Pattern Recognition Workshop (AIPR), 2011 IEEE. – 2011. – P. 1-8.
  10. Soofbaf, S.R. Anomaly detection algorithms for hyperspectral imagery / S.R. Soofbaf, H. Fahimnejad, M.J. Valadan Zoej, B. Mojaradi // Proceedings, Remote Sensing and Image Processing, Presented at the Map of the World Forum. – 2007. – P. 1-8.
  11. Schaum, A.P. Hyperspectral anomaly detection beyond RX / A.P. Schaum // Proceedings of the SPIE Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XII. – 2007. – V. 6565.
  12. Messinger, D.W. A graph theoretic approach to anomaly detection in hyperspectral imagery / D.W. Messinger, J. Albano // Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011 3rd Workshop on. – 2011. – P. 1-4.
  13. Banerjee, A. A support vector method for anomaly detection in hyperspectral imagery / A. Banerjee, P. Burlina, C. Diehl // Geoscience and Remote Sensing, IEEE Transactions on. – 2006. – V. 44(8). – P. 2282-2291.
  14. Gu, Y. A selective KPCA algorithm based on high-order statistics for anomaly detection in hyperspectral imagery / Y. Gu, Y. Liu, Y. Zhang // Geoscience and Remote Sensing Letters. – 2008. – V. 5(1). – P. 43-47.
  15. Basener, D. Anomaly detection using topology / B. Basener, E. Ientilucci, D.W. Messinger // Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII. – 2007. – V. 6565.
  16. Computer Image Processing, Part II: Methods and algorithms / A.V. Chernov, V.M. Chernov, M.A. Chicheva, V.A. Fursov, M.V. Gashnikov, N.I. Glumov, N.Yu. Ilyasova, A.G. Khra­mov, A.O. Korepanov, A.V. Kupriyanov, E.V. Myasnikov, V.V. Myas­nikov, S.B. Popov, V.V. Sergeyev. – Ed. by V.A. Soifer. – VDM Verlag, 2009. – 584 p.
  17. Kostrikin, A.I. Linear algebra and geometry / A.I. Kostri­kin, Yu.I. Manin. – 4th Edition. – Petersburg, 2008. – (In Russian).
  18. Birkhoff, G. Modern applied algebra / G. Birkhoff, T. Bartee. – Moscow: “Mir” Publisher, 1976. – 400 p. – (In Russian).
  19. Clark, R.N. The U. S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns, U.S. Geological Survey Open File Report 93-592 / R.N. Clark, G.A. Swa­yze, A.J. Gallagher, T.V.V. King, W.M. Calvin. – 1993. – 1340 p.
  20. Rodarmel, C. Principal component analysis for hyperspectral image classification / C. Rodarmel, J. Shan // Surveying and Land Information Science. – 2002. – V. 62(2). – P. 115-122.
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