Analytical comparison of the matched filter and orthogonal subspace projection detectors for hyperspectral images

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 7, 2007, Pages 2394-2402

  Bajorski, Peter ^a,b  

^a IEEE   (United States)

^b Kate Gleason College of Engineering   (United States)

Author keywords

Detection power; Hyperspectral image; Linear mixing model (LMM); Matched filter; Orthogonal subspace projection (OSP); Structured background; Target detection

Indexed keywords

DETECTION POWER; HYPERSPECTRAL IMAGE; LINEAR MIXING MODEL (LMM); MATCHED FILTER; ORTHOGONAL SUBSPACE PROJECTION (OSP); STRUCTURED BACKGROUND;

LINEAR SYSTEMS; MATHEMATICAL MODELS; PROJECTION SYSTEMS; TARGET TRACKING;

IMAGE ANALYSIS;

EID: 34347209724     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.896544     Document Type: Conference Paper

References (18)

1. C.I Chang, X.-L. Zhao, M. L. G. Althouse, and J. J. Pan, "Least squares subspace projection approach to mixed pixel classification for hyper-spectral images," IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 898-912, May 1998.
2. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002.
3. L. L. Scharf and B. Friedlander, "Matched subspace detectors," IEEE Trans. Signal Process., vol. 42, no. 8, pp. 2146-2156, Aug. 1994.
4. S. Johnson, "The relationship between the matched filter operator and the target signature space orthogonal projection classifier," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 283-286, Jan. 2000.
5. T. M. Tu, C.-H. Chen, and C.-I Chang, "A least squares orthogonal subspace projection approach to desired signature extraction and detection," IEEE Trans. Geosci. Remote Sens., vol. 35, no. 1, pp. 127-139, Jan. 1997.
6. C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer/Plenum, 2003.
7. J. Harsanyi and C.-I Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
8. J. J. Settle, "On the relationship between spectral unmixing and subspace projection," IEEE Trans. Geosci. Remote Sens., vol. 34, no. 4, pp. 1045-1046, Jul. 1996.
9. R. T. Behrens and L. L. Scharf, "Signal processing applications of oblique projections operators," IEEE Trans. Signal Process., vol. 42, no. 6, pp. 1413-1424, Jun. 1994.
10. S. Johnson, "Constrained energy minimization and the target-constrained interference-minimization filter," Opt. Eng., vol. 42, no. 6, pp. 1850-1854, Jun. 2003.
11. N. R. Draper and H. Smith, Applied Regression Analysis, 3rd ed. Hoboken, NJ: Wiley, 1998.
12. S. Johnson, "The constrained signal detector," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1326-1337, Jun. 2002.
13. C.-I Chang, "Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 502-518, Mar. 2005.
14. C.-I Chang, J. Liu, B. Chieu, H. Ren, C. Wang, C. Lo, P. Chung, C. Yang, and D. Ma, "Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery," Opt. Eng., vol. 39, no. 5, pp. 1275-1281, May 2000.
15. H. Ren and C.-I Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images," Opt. Eng., vol. 39, no. 12, pp. 3138-3145, Dec. 2000.
16. S.R. Sentle, Linear Models. Hoboken, NJ: Wiley, 1971.
17. R. Behrens and L. Scharf, "Parameter estimation in the presence of low rank noise," in Proc. 22nd Asilomar Conf. Signals, Syst., Comput., 1988, pp. 341-344.
18. J. Hajek, Z. Sidak, and P. Sen, Theory of Rank Tests. San Diego, CA: Academic, 1999.