Matched filter stochastic background characterization for hyperspectral target detection

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5806, Issue PART I, 2005, Pages 1-12

  West, Jason E ^a   Messinger, David W ^a   Ientilucci, Emmett J ^a   Kerekes, John P ^a   Schott, John R ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Background characterization; Covariance; Hyperspectral; Matched filters; Target detection

Indexed keywords

BACKGROUND CHARACTERIZATION; COVARIANCE; HYPERSPECTRAL IMAGERY; MATCHED FILTERS; TARGET DETECTION;

GEOMETRY; IMAGE SENSORS; MATHEMATICAL MODELS; STATISTICAL METHODS;

ALGORITHMS;

EID: 27544456023     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.605711     Document Type: Conference Paper

References (13)

1. C.C. Funk, J. Theiler, D.A. Roberts and C.C. Borel, "Clustering to Improve Matched Filter Detection of Weak Gas Plumes in Hyperspectral Thermal Imagery," IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 1410-1420, July 2001.
2. D. Manolakis and G. Shaw, "Detection Algorithms for Hyperspectral Imaging Applications," IEEE Signal Processing Magazine, 19, pp. 29-43, January 2002.
3. I.S. Reed and X. Yu, "Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution", IEEE Transactions on Acoustics, Speech and Signal Processing, 38-10, pp. 1760-1770, October 1990.
4. Y. Li, A. Vodecek, R. Kremens, and A. Ononye, "A New Algorithm for Global Forest Fire Detection Using Multispectral Images," Targets and Background IX, Characterization and Representation, Proceedings of SPIE, 5075, pp. 367-377, April 2003.
5. W.H. Ferrand and J.C. Harsanyi, "Mapping the distribution of mine tailings in the Coeur di Alene River Valley, Idaho through the use of a constrained energy minimization technique," Remote Sensing of Environment, 59, pp. 64-76, 1997.
6. J. Schott, Remote Sensing: The Image Chain Approach. Oxford University Press, New York, NY, 1997.
7. E.A. Ashton, "Detection of Subpixel Anomalies in Multispectral Infrared Imagery Using an Adaptive Bayesian Classifier," IEEE Transactions on Geoscience and Remote Sensing, 36-2, pp. 506-517, March 1998.
8. P. Masson and W. Pieczynski, "SEM Algorithm and Unsupervised Statistical Segmentation of Satellite Images," IEEE Transactions on Geoscience and Remote Sensing, 33-3, pp. 618-633, May 1993.
9. D.W.J. Stein, A. Stocker, S. Beaven, "The Fusion of Quadratic Detection Statistics Applied to Hyperspectral Imagery," SPAWAR Systems Center San Diego, Sponsored by CECOM RDEC Night Vision and Electronic Sensors Directorate, January 2001.
10. B-C Kuo and D.A. Landgrebe, "A Covariance Estimator for Small Sample Size Classification Problems and Its Application to Feature Extraction", IEEE Transactions on Geoscience and Remote Sensing, 40-4, pp. 814-819, April 2002.
11. P. Bajorski, E.J. Ientilucci, and J.R. Schott, "Comparison of Basis-Vector Selection Methods for Target and Background Subspaces as Applied to Subpixel Target Detection", Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, Proceedings of SPIE, 5425, pp. 97-108, April 2004.
12. C.J. Mecklin and D.J. Mendfrom, "An Appraisal and Bibliography of Tests for Multivariate Normality", International Statistical Review / Revue Internationale de Statistique, 72, no. 1, pp. 123-138, April 2004.
13. G.W. Snedecor and W.G. Cochran, Statistical Methods. Eighth Edition, Iowa State University Press, 1989.