Continuum fusion methods of spectral detection

Optical Engineering

Volumn 51, Issue 11, 2012, Pages

  Schaum, Alan P ^a   Daniel, Brian J ^a  

^a NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Clairvoyant; Continuum fusion; Detection; Outlier; Parameters; Spectral; Subspace

Indexed keywords

CLAIRVOYANT; CONTINUUM FUSIONS; OUTLIER; PARAMETERS; SPECTRAL; SUBSPACE;

ERROR DETECTION; MOLECULAR PHYSICS;

ENGINEERING;

EID: 84881149211     PISSN: 00913286     EISSN: 15602303     Source Type: Journal    
DOI: 10.1117/1.OE.51.11.111718     Document Type: Article

References (18)

1. D. B. Marden and D. Manolakis, "Using elliptically contoured distributions to model hyperspectral imaging data and generate statistically similar synthetic data," Proc. SPIE 5425, 558-572 (2002).
2. A. Schaum, "Continuum fusion, a theory of inference, with applications to hyperspectral detection," Opt. Express 18(8), 8171-8181 (2010).
3. S. Kay, Fundamentals of Statistical Signal Processing, Vol. 2, Detection Theory, Prentice Hall, Upper Saddle River, NJ (1998).
4. A. Schaum, "Algorithms with attitude," in Proc. of the 39th IEEE Applied Imagery Pattern Recognition Workshop, pp. 1- 6, IEEE Computer Society, Washington, DC (2010).
5. 3 R) algorithm for spectral detection," Proc. SPIE 8048, 804804 (2011).
6. A. Schaum, "The continuum fusion theory of signal detection applied to a bi-modal fusion problem," Proc. SPIE 8064, 806403 (2011).
7. A. Schaum and B. J. Daniel, "Next generation signature-based hyperspectral detection: a challenge to atmospheric modelers," Proc. SPIE 8040, 80400C (2011).
8. A. Schaum, "Continuum fusion: a new methodology for overcoming imperfect sensors and the limitations of statistical detection models," in Proc. of the MSS Meeting on Passive Sensing, pp. 1-12, SENSIAC, Orlando, FL (2011).
9. B. Daniel and A. Schaum, "A new flavor of continuum fusion detector applied to hyperspectral imagery," in Proc. of the MSS Meeting on Battlefield Survivability and Defense, pp. 1-9, SENSIAC, Orlando, FL (2011).
10. A. Schaum, "Continuum fusion: a new methodology for creating hyperspectral detection algorithms," Proc. SPIE 7695, 769502 (2010).
11. P. Bajorksi, "Min-max detection fusion for hyperspectral images," in IEEE Workshop on Hyperspectral Imaging and Signal Processing Evolution in Remote Sensing, to be published.
12. L. Scharf, "The adaptive coherence estimator: a uniformly most-power-ful- invariant adaptive detection statistic," IEEE Trans. Signal Process. 53(2), 427-438 (2005).
13. A. Schaum, "CFAR fusion: a replacement for the generalized likelihood ratio test for Neyman-Pearson problems," in Proc. of the 40th IEEE Applied Imagery Pattern Recognition Workshop, pp. 1-6, IEEE Computer Society, Washington, DC (2011).
14. A. Schaum and R. Priest, "The vicarious relative calibration method of spectral target detection," in Proc. of the SENSIAC Military Sensing Symp., Battlefield Survivability and Discrimination, Washington, DC, pp. 1-8 (2009).
15. B. R. Foy, J. Theiler, and A. M. Fraser, "Decision boundaries in two dimensions for target detection in hyperspectral imagery," Opt. Express 17(20), 17391-17411 (2009).
16. A. Schaum et al., "Multispectral detection concepts for maritime domain awareness," in Proc. of the SENSIAC Military Sensing Symp., Battlefield Survivability and Discrimination, Pasadena, CA, pp. 1-10 (2012).
17. A. Schaum, "Progress in the theory of continuum fusion," Proc. SPIE 8390, 839002 (2012).
18. M. Feder and N. Merhav, "Universal composite hypothesis testing: a competitive minimax approach," IEEE Trans. Info. Theory 48(6), 1504-1517 (2002).