CEM: More bands, better performance

IEEE Geoscience and Remote Sensing Letters

Volumn 11, Issue 11, 2014, Pages 1876-1880

  Geng, Xiurui ^a   Ji, Luyan ^b   Sun, Kang ^a   Zhao, Yongchao ^a  

^a INSTITUTE OF ELECTRONICS   (China)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

Band number; constrained energy minimization (CEM); hyperspectral data; receiver operating characteristic (ROC)

Indexed keywords

IMAGE PROCESSING; SPECTROSCOPY;

BAND NUMBER; BETTER PERFORMANCE; CONSTRAINED ENERGY MINIMIZATION; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGE PROCESSING; ORIGINAL IMAGES; RECEIVER OPERATING CHARACTERISTICS; TARGET DETECTION ALGORITHM;

COMPUTATIONAL ELECTROMAGNETICS;

EID: 84901853592     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2014.2312319     Document Type: Article

References (27)

1. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002. (Pubitemid 34237206)
2. X. R. Geng and Y. C. Zhao, "Principle of small target detection for hyperspectral imagery," Sci. Chin. Ser. D, Earth Sci., vol. 50, no. 8, pp. 1225-1231, Aug. 2007. (Pubitemid 47062617)
3. F. A. Kruse, A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, "The Spectral Image Processing System (SIPS)-Interactive visualization and analysis of imaging spectrometer data," Remote Sens. Environ., vol. 44, no. 2/3, pp. 145-163, May/Jun. 1993. (Pubitemid 23383101)
4. R. N. Clark, G. A. Swayze, A. Gallagher, N. Gorelick, and F. Kruse, "Mapping with imaging spectrometer data using the complete band shape least squares algorithm simultaneously fit to multiple spectral features from multiple materials," in Proc. 3rd Airborne Visible/Infrared Imaging Spectr. Workshop, 1991, pp. 2-3.
5. J. C. Harsanyi, "Detection and classification of subpixel spectral signatures in hyperspectral image sequences," Ph.D. dissertation, Dept. Elect. Eng., Univ. Maryland, Baltimore, MD, USA, 1993.
6. Q. Du, H. Ren, and C.-I. Chang, "A comparative study for orthogonal subspace projection and constrained energy minimization," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1525-1529, Jun. 2003.
7. M. K. Arora, S. Bansal, S. Khare, and K. Chauhan, "Comparative assessment of some target detection algorithms for hyperspectral images," Defence Sci. J., vol. 63, no. 1, pp. 53-62, Jan. 2013.
8. W. H. Farrand and J. C. Harsanyi, "Mapping the distribution of mine tailings in the coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique," Remote Sens. Environ., vol. 59, no. 1, pp. 64-76, Jan. 1997.
9. D. Manolakis, G. Shaw, and N. Keshava, "Comparative analysis of hyperspectral adaptive matched filter detector," in Proc. SPIE, 2000, pp. 2-17.
10. J. Y. Chen and I. S. Reed, "A detection algorithm for optical targets in clutter," IEEE Trans. Aerosp. Electron. Syst., vol. AES-23, no. 1, pp. 46-59, Jan. 1987.
11. D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, "Is there a best hyperspectral detection algorithm?" in Proc. SPIE, 2009, pp. 733402-1-733402-16.
12. D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, "Hyperspectral detection algorithms: Use covariances or subspaces," in Proc. SPIE, 2009, pp. 74570Q-1-74570Q-8.
13. R. S. DiPietro, D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, "Performance evaluation of hyperspectral detection algorithms for subpixel objects," in Proc. SPIE, 2010, p. 76951W.
14. D. Manolakis, D. Marden, J. Kerekes, and G. Shaw, "On the statistics of hyperspectral imaging data," in Proc. SPIE, 2001, pp. 308-316. (Pubitemid 33060184)
15. 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 Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1410-1420, Jul. 2001. (Pubitemid 32736637)
16. J. Minet, J. Taboury, F. Goudail, M. Péalat, N. Roux, J. Lonnoy, and Y. Ferrec, "Influence of band selection and target estimation error on the performance of the matched filter in hyperspectral imaging," Appl. Opt., vol. 50, no. 22, pp. 4276-4284, Aug. 2011.
17. J. W. Boardman, "Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target un-mixing and rejection of false positives: Mixture tuned matched filtering," in Proc. Summaries 7th Annual JPL Airborne Geosci. Workshop, 1998, p. 55.
18. J. W. Boardman and F. A. Kruse, "Analysis of imaging spectrometer data using N-dimensional geometry and a Mixture-Tuned Matched Filtering (MTMF) approach," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4138-4152, Nov. 2011.
19. J. J. Mitchell and N. F. Glenn, "Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.)," Int. J. Remote Sens., vol. 30, no. 23, pp. 6099-6119, Nov. 2009.
20. I. Dópido, A. Villa, A. Plaza, and P. Gamba, "A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 421-435, Apr. 2012.
21. H. Ren and C.-I. Chang, "A generalized orthogonal subspace projection approach to unsupervised multispectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 6, pp. 2515-2528, Nov. 2000.
22. C.-I. Chang, J.-M. Liu, B.-C. Chieu, C.-M. Wang, C.-I Chang, C.-W. Yang, D.-J. Ma, C.-S. Lo, and P.-C. Chung, "Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery," Opt. Eng., vol. 39, no. 5, pp. 1275-1281, May 2000.
23. J. Sherman and W. J. Morrison, "Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix (abstract)," Ann. Math. Stat., vol. 20, p. 621, 1949.
24. J. Sherman and W. J. Morrison, "Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix," Ann. Math. Stat., vol. 21, pp. 124-127, 1950.
25. X. Geng, Z. Xiao, L. Ji, Y. Zhao, and F. Wang, "A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery," ISPRS J. Photogramm. Remote Sens., vol. 79, pp. 211-218, May 2013.
26. S. Wang and C.-I. Chang, "Variable-number variable-band selection for feature characterization in hyperspectral signatures," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 9, pp. 2979-2992, Sep. 2007. (Pubitemid 47331324)
27. C.-I. Chang, "Target signature-constrained mixed pixel classification for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1065-1081, May 2002. (Pubitemid 34798916)