An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 38, Issue 2 II, 2000, Pages 1044-1063

  Chang, Chein I ^a   Ren, Hsuan ^a  

^a Baltimore County   (United States)

Author keywords

Linear discriminant analysis (LDA); Linear unmixing; Maximum likelihood estimator (MLE)

Indexed keywords

ALGORITHMS; IMAGE CONVERTERS; MAXIMUM LIKELIHOOD ESTIMATION; PATTERN RECOGNITION; SPECTRUM ANALYSIS;

HYPERSPECTRAL IMAGERY; IMAGE CLASSIFICATION ALGORITHM; LINEAR DISCRIMINANT ANALYSIS; ORTHOGONAL SUBSPACE PROJECTION; SIGNATURE SPACE PROJECTION; TARGET DETECTION;

IMAGE ANALYSIS;

EID: 0033872604     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/36.841984     Document Type: Article

References (38)

1. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
2. J. G. Moik, Digital Processing off Remotely Sensed Images. Washington, DC: NASA SP-431, 1980.
3. R. A. Schowengerdt, Techniques for Image Processing and Classification in Remote Sensing. New York: Academic, 1983.
4. J. A. Richards, Remote Sensing Digital Image Analysis, 2nd ed. Berlin, Germany: Springer-Verlag, 1993.
5. J. R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
6. 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, pp. 735-743, Mar. 1998.
7. C.-I Chang and Q. Du, Interference and noise adjusted principal components analysis, IEEE Trans. Geosci. Remote Sensing, vol. 37, pp. 2387-2396, Sept. 1999.
8. S. D. Zenzo, S. D. Degloria, R. Bernstein, and H. C. Kolsky, Gaussian maximum likelihood and contextual classification algorithms for multicrop classification, IEEE Trans. Geosci. Remote Sensing, vol. GE25, pp. 805-814, Nov. 1987.
9. S. D. Zenzo, R. Bernstein, S. D. Degloria, and H. C. Kolsky, Gaussian maximum likelihood and contextual classification algorithms for multicrop classification experiments using thematic mapper and multispectral scanner sensor data, IEEE Trans. Geosci. Remote Sensing, vol. GE25, pp. 815-824, Nov. 1987.
10. B. Kim and D. A. Landgrebe, Hierarchical classifier design in high-dimensional, numerous class cases, IEEE Trans. Geosci. Remote Sensing, vol. 29, pp. 792-800, July 1991.
11. C. Lee and D. A. Landgrebe, Analyzing high-dimensional multispectral data, IEEE Trans. Geosci. Remote Sensing, vol. 31, pp. 792-800, July 1993.
12. B. M. Shahshahani and D. A. Landgrebe, The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hugh phenomenon, IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 1087-1095, Sept. 1994.
13. X. Jia and J. A. Richards, Efficient maximum likelihood classification for imaging spectrometer data sets, IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 274-281, Mar. 1994.
14. C. Lee and D. A. Landgrebe, Feature extraction based on decision boundaries, IEEE Trans. Pattern Anal. Machine Intell, vol. 15, no. 4, pp. 388-400, Apr. 1993.
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. J. Harsanyi, Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences, Ph.D. Dissertation, Dept. Elect. Eng., Univ. Maryland, Baltimore County, Aug. 1993.
17. DARPA Spectral Exploitation Workshop, The Defense Adavanced Research Projects Agency, Annapolis, MD, July 1-2, 1996.
18. C. Brumbley and C.-I Chang, An unsupervised vector quantization-based target signature subspace projection approach to classification and detection in unknown background, Pattern Recognit., vol. 32, pp. 1161-1174, July 1999.
19. C.-I Chang, Q. Du, T. S. Sun, and M. L. G. Althouse, A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification, IEEE Trans. Geosci. Remote Sensing, vol. 37, pp. 2631-2641, Nov. 1999.
20. T. M. Tu, C. H. Chen, and C.-I Chang, A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection, IEEE Trans. Geosci. Remote Sensing, vol. 35, pp. 127-139, Jan. 1997.
21. C.-I Chang, X. Zhao, M. L. G. Althouse, and J.-J. Pan, Least squares subspace projection approach to mixed pixel classification in hyperspectral images, IEEE Trans. Geosci. Remote Sensing, vol. 36, pp. 898-912, May 1998.
22. H. Ren and C.-I Chang, A computer-aided detection and classification method for concealed targets in hyperspectral imagery, in Int. Symp. Geoscience and Remote Sensing'98, Seattle, WA, July 5-10, 1998, pp. 1016-1018.
23. J. J. Settle, On the relationship between spectral unmixing and subspace projection, IEEE Trans. Geosci. Remote Sensing, vol. 34, pp. 1045-1046, July 1996.
24. C.-I Chang, Further results on relationship between spectral unmixing and subspace projection, IEEE Trans. Geosci. Remote Sensing, vol. 36, pp. 1030-1032, May 1998.
25. J. B. Adams and M. O. Smith, Spectral mixture modeling: A new analysis of rock and soil types at the Viking lander 1 suite, J. Geophys. Res., vol. 91, pp. 8098-8112, July 10, 1986.
26. 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.
27. J. W. Boardman, Inversion of imaging spectrometry data using singular value decomposition, in Proc. IEEE Symp. Geoscience and Remote Sensing, 1989, pp. 2069-2072.
28. R. T. Behrens and L. L. Scharf, Signal processing applications of oblique projections operators, IEEE Trans. Signal Processing, vol. 42, pp. 1413-1423, June 1994.
29. T. M. Tu, H. C. Shy, C.-H. Lee, and C.-I Chang, An oblique subspace projection to mixed pixel classification in hyperspectral images, Pattern Recognit., vol. 32, pp. 1399-1408, Aug. 1999.
30. C. Brumbley and C.-I Chang, Unsupervised linear unmixing Kaiman filtering approach to signature extraction and estimation for remotely sensed images, in Int. Symp. Geoscience and Remote Sensing'98, Seattle, WA, July 5-10, 1998, pp. 1590-1592.
31. H. Ren, A Comparative Study of Mixed Pixel Classification versus Pure Pixel Classification for Multi/Hyperspectral Imagery, M.S. Thesis, Dept. Comp. Sci. Elect. Eng., Univ. Maryland, Baltimore County, May 1998.
32. S. Haykin, Neural Networks: Macmillan, 1994.
33. K. Fukunaga, Statistical Pattern Recognition, 2nd ed. New York: Academic, 1990.
34. C. I Chang, Least squares error theory for linear mixing problems with mixed pixel classification for hyperspectral imagery, Recent Res. Devel. Opt. Eng., vol. 2, pp. 241-268, 1999.
35. D. Heinz and C.-I Chang, Subpixel spectral detection for remotely sensed images, , to be published.
36. H. Ren and C.-I Chang, A generalized orthogonal subspace projection approach to unsupervised multispectral image classification, SPIE Conf. Image and Signal Processing for Remote Sensing IV, vol. 3500, pp. 42-53, Sept. 21-25, 1998.
37. P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, A survey of thresholding techniques, Comput. Vis., Graph. Image Process. (CVGIP), vol. 41, pp. 233-260, 1988.
38. D. Heinz and C.-I Chang, Fully constrained least squares-based linear unmixing, in Int. Geoscience and Remote Sensing Symp. '99, Hamburg, Germany, June 28-July 2, 1999, pp. 1401-1403.