Constrained subpixel target detection for remotely sensed imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 38, Issue 3, 2000, Pages 1144-1159

  Chang, Chein I ^a  

^a University of Marylandw Baltimore County   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONSTRAINT THEORY; EIGENVALUES AND EIGENFUNCTIONS; INFRARED SPECTROMETERS; LEAST SQUARES APPROXIMATIONS; REMOTE SENSING; SPECTRUM ANALYSIS;

AIRBORNE VISIBLE INFRARED IMAGING SPECTROMETER; CONSTRAINED ENERGY MINIMIZATION; HYPERSPECTRAL DIGITAL IMAGERY COLLECTION EXPERIMENT; LINEAR SPECTRAL MIXTURE ANALYSIS; NONNEGATIVELY CONSTRAINED LEAST SQUARES METHOD; ORTHOGONAL SUBSPACE PROJECTION; SPATIAL IMAGE ANALYSIS; SUBPIXEL TARGET DETECTION;

IMAGE ANALYSIS;

EID: 0033719885     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/36.843007     Document Type: Article

References (28)

1. D. E. Sabol, J.B. Adams, and M. O. Smith, "Quantitative sub-pixel spectral detection of targets in multispectral images," J. Geophys. Res., vol. 97, pp. 2659-2672, 1992.
2. M. O. Smith, J. B. Adams, and D. E. Sabol, "Spectral mixture analysis-new strategies for the analysis of multispectral data," in Image Spectroscopy-A Tool for Environmental Observations, J. Hill and J. Mergier, Eds. Brussels, Belgium: ECSC, EEC, EAEC, 1994, pp. 125-143.
3. J. B. Adams, M. O. Smith, and A. R. Gillespie, "Image spectroscopy: Interpretation based on spectral mixture analysis," in Remote Geochemical Analysis: Elemental and Mineralogical Composition, C. M. Pieters and P. A. Englert, Eds. Cambridge, U.K.: Cambridge Univ. Press, 1993, pp. 145-166.
4. A. R. Gillespie, M. O. Smith, J. B. Adams, S. C. Willis, A. F. Fischer III, and D. E. Sabol, "Interpretation of residual images: Spectral mixture analysis of AVIRIS images," in Proc. 2nd AVIRIS Workshop, Owens Valley, CA, 1990, pp. 243-270.
5. J. B. Adams, M. O. Smith, and A. R. Gillepie, "Simple models for complex natural surfaces: A strategy for hyperspectral era of remote sensing," in Proc. IEEE Int. Geoscience and Remote Sensing Symp. '89, 1989, pp. 16-21.
6. S. Tompkins, J. F. Mustarrd, C. M. Pieters, and D. W. Forsyth, "Optimization of endmembers for spectral mixture analysis," Remote Sens. Environ., vol. 59, pp. 472-489, 1997.
7. J. W. Boardman, "Inversion of imaging spectrometry data using singular value decomposition," in Proc. IEEE Symp. Geoscience and Remote Sensing, 1989, pp. 2069-2072.
8. J. J. Settle and N. A. Drake, "Linear mixing and estimation of ground cover proportions," IntJ. Remote Sensing, vol. 14, no. 6, pp. 1159-1177, 1993.
9. Y. E. Shimabukuro and J. A. Smith, "The least-squares mixing models to generate fraction images derived from remote sensing multispectral data," IEEE Trans. Geosci. Remote Sensing, vol. 29, pp. 16-20, Jan. 1991.
10. M. O. Smith, D. A. Roberts, J. Hill, W. Mehl, B. Hosgood, J. Verdebout, G. Schmuck, C. Koechler, and J. B. Adams, "A new approach to quantifying abundances of materials in multispectral images," in Proc. IEEE Int. Geoscience and Remote Sensing Symp. '94, Pasadena, CA, August 1994, pp. 2372-2374.
11. J. W. Boardman, "Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positive: Mixture tuned matched filtering," Proc. 7th Annu. JPL Earth Science Workshop, 1998.
12. J. C. Harsanyi, "Detection and classification of subpixel spectral signatures in hyperspectral image sequences," Ph.D. dissertation, Dept. Elect. Eng., Univ. Maryland, Baltimore County, 1993.
13. E. A. Ashtonand A. Schaum, "Algorithms for the detection of sub-pixel targets in multispectral imagery," Photogramm. Eng. Remote Sensing, pp. 723-731, July 1998.
14. D. Heinz, C.-I Chang, and M. L. G. Althouse, "Fully constrained least squares-based linear unmixing," in Int. Geoscience and Remote Sensing Symp. '99, Hamburg, Germany, June 28 - July 2 1999.
15. J. C. Harsanyi and C.-I Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection," IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 779-785, July 1994.
16. C.-I Chang, T.-L. E. Sun, and M. L. G. Althouse, "An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery," Opt. Eng., vol. 37, no. 3, pp. 735-743, March 1998.
17. C. L. Lawson and R. J. Hanson, "Solving least squares problems," in Proc. Classics in Applied Mathematics. Philadelphia, PA, 1995.
18. R. Bro and S. D. Jong, "A fast nonnegativity-constrained least squares algorithm," J. Chemom., vol. 11, pp. 393-401, 1997.
19. J. C. Harsanyi, W. Farrand, and C.-I Chang, "Detection of subpixel spectral signatures in hyperspectral image sequences," in Proc. American Society of Photogrammetry and Remote Sensing, Reno, NV, 1994, pp. 236-247.
20. W. W. Farrand and J. C. Harsanyi, "Mapping the distribution of mine tailing in the coeur d'Alene River Valley, Idaho, through the use of constrained energy minimization technique," Remote Sens. Environ., vol. 59, pp. 64-76, 1997.
21. B. D. Van Veen and K. M. Buckley, "Beamforming: A versatile approach to spatial filtering," IEEE ASSP Mag., Apr. 4-24, 1988.
22. S. Haykin, Adaptive Filter Theory, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
23. M. L. G. Althouse and C.-I Chang, "Chemical vapor detection with a multispectral thermal imager," Opt. Eng., vol.30, no. 11, pp. 1725-1733, July 1991.
24. O. L. Frost, III, "An algorithm for linearly constrained adaptive array processing," Proc. IEEE, vol. 60, pp. 926-935, 1972.
25. C.-I Chang and H. Ren, "Linearly constrained minimum variance beam-forming for target detection and classification in hyperspectral imagery," in Int. Geoscience and Remote Sensing Symp. '99, Hamburg, Germany, June 28 - July 2 1999.
26. J. Harsanyi, W. Farrand, and C.-I Chang, "Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization," in Proc. 9th Thematic Conf. Geologic Remote Sensing, Pasadena, CA, 1993.
27. C.-I Chang and Q. Du, "A noise subspace projection approach to determination of intrinsic dimensionality of hyperspectral imagery," in EOS/SPIE Symp. Remote Sensing, Conf. Image and Signal Processing for Remote Sensing V, Florence, Italy, September 20-24, 1999.
28. D. Heinz and C.-I Chang, Constrained Least Squares Methods for Material Detection and Quantification of Linearly Mixing Problems, Disclosure and Invention. Baltimore, MD: Univ. Maryland, Baltimore County, May 1999.