Exploiting manifold geometry in hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 43, Issue 3, 2005, Pages 441-454

  Bachmann, Charles M ^a   Ainsworth, Thomas L ^a   Fusina, Robert A ^a  

^a NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Airborne Visible Imaging Spectrometer (AVIRIS); Bidirectional reflectance distribution function (BRDF); Compression; Cuprite; Geodesic distance; Hyperspectral; Isometric mapping (ISOMAP); Land cover classification; Linear mixture; Local linear embedding (LLE)

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; DATA COMPRESSION; DATA REDUCTION; MAPPING; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; REMOTE SENSING;

AIRBORNE VISIBLE IMAGING SPECTROMETER (AVIRIS); BIDIRECTIONAL REFLECTANCE DISTRIBUTION FUNCTION (BRDF); CUPRITE; GEODESIC DISTANCE; HYPERSPECTRAL; ISOMETRIC MAPPING (ISOMAP); LAND-COVER CLASSIFICATION; LINEAR MIXTURE; LOCAL LINEAR EMBEDDING (LLE); VIRGINIA COAST RESERVE;

IMAGE ANALYSIS;

EID: 14644422171     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2004.842292     Document Type: Conference Paper

References (46)

1. D. K. Agrafiotis, "Stochastic proximity embedding," J. Comput. Chem., vol. 24, pp. 1215-1221, 2003.
2. D. K. Agrafiotis and H. Xu, "A self-organizing principle for learning nonlinear manifolds," Proc. Nat. Acad. Sci., vol. 99, no. 25, pp. 15 869-15872, Dec. 2002.
3. T. L. Ainsworth and J. S. Lee, "Optimal image classification employing optimal polarimetric variables," in Proc. IGARSS, vol. 2, Toulouse, France, 2003, pp. 696-698.
4. _, "Optimal polarimetric decomposition variables - Non-linear dimensionality reduction," in Proc. IGARSS, vol. 2, Sydney, Australia, 2001, pp. 928-930.
5. C. M. Bachmann, "Improving the performance of classifiers in high-dimensional remote sensing applications: An adaptive resampling strategy for error-prone exemplars (ARESEPE)," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 2101-2112, Sep. 2003.
6. C. M. Bachmann, T. F. Donato, G. M. Lamela, W. J. Rhea, M. H. Bettenhausen, R. A. Fusina, K. DuBois, J. H. Porter, and B. R. Truitt, "Automatic classification of land-cover on Smith Island, VA using HYMAP imagery," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2313-2330, Oct. 2002.
7. C. M. Bachmann, M. H. Bettenhausen, R. A. Fusina, T. F. Donato, A. L. Russ, J. Burke, G. M. Lamela, W. J. Rhea, B. R. Truitt, and J. H. Porter, "A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 11, pp. 2488-2499, Nov. 2003.
8. M. Balasubramanian, E. L. Schwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford, "The ISOMAP algorithm and topological stability," Science, vol. 295, no. 5552, p. 7a, Jan. 2002.
9. R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. M. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. Van der Vorst. (1994) Templates for the solution of linear systems building blocks for iterative methods. SIAM, Philadelphia, PA. [Online]. Available: http://www.netlib.org/templates/ Templates.html.
10. J. W. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," in Proc. 4th Annu. JPL Airborne Geoscience Workshop, vol. 1, Pasadena, CA, 1993, JPL Pub. 93-26, pp. 11-14.
11. _, "Analysis, understanding and visualization of hyperspectral data as convex sets in n-space," Proc. SPIE, vol. 2480, 1995.
12. J. W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Summaries 5th JPL Airborne Earth Science Workshop, vol. 1, 1995, JPL Pub. 95-1, pp. 23-26.
13. J. Bowles, P. Palmadesso, J. Antoniades, M. Baumback, and L. J. Rickard, "Use of filter vectors in hyperspectral data analysis," Proc. SPIE, vol. 2553, pp. 148-157, 1995.
14. J. Bowles, M. Daniel, J. Grossman, J. Antoniades, M. Baumback, and P. Palmadesso, "Comparison of output from ORASIS and pixel purity calculations," Proc. SPIE, vol. 3438, pp. 148-156, 1998.
15. J. Bowles, D. Gillis, and P. Palmadesso, "New improvements in the ORASIS algorithm," in Proc. IEEE Aerospace Conf., vol. 3, 2000, pp. 293-298.
16. C.-I Chang and S. Chiang, "Anomaly detection and classification for hyperspectral imagery," IEEE Trans. Geosci. and Remote Sens., vol. 40, no. 6, pp. 1314-1325, Jun. 2002.
17. R. N. Clark, G. A. Swayze, C. Koch, A. Gallagher, and C. Ager, "Mapping vegetation types with the multiple spectral feature mapping algorithm in both emission and absorption," in Summaries of the 3rd Annu. JPL Airborne Geosciences Workshop, vol. 1, 1992, AVIRIS Workshop JPL Pub. 92-14, pp. 60-62.
18. "Mapping minerals with imaging spectroscopy," U.S. Geol. Surv., Office of Mineral Resources, Reston, VA, Bull. 2039, 1993.
19. D. Gillis, P. Palmadesso, and J. Bowles, "Automatic target recognition system for hyperspectral imagery using ORASIS," Proc. SPIE, vol. 4381, pp. 34-34, 2001.
20. E. W. Dijkstra, "A note on two problems in connexion with graphs," Num. Math., vol. 1, pp. 269-271, 1959.
21. D. L. Donoho and C. Grimes, "Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data," Proc. Nat. Acad. Sci., vol. 100, no. 10, pp. 5591-5596, May 2003.
22. D. G. Goodin, J. Gao, and G. M. Henebry, "The effect of solar illumination angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 1, pp. 154-165, Jan. 2004.
23. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988.
24. K. Guilfoyle, M. L. Althouse, and C.-I. Chang, "Further investigations into the use of linear and nonlinear mixing models for hyperspectral image analysis," Proc. SPIE, vol. 4725, pp. 157-167, 2002.
25. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. New York: Springer-Verlag, 2001.
26. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
27. F. A. Kruse, A. B. Lefkoff, J. W. 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, pp. 145-163, 1993.
28. V. Kumar, A. Grama, A. Gupta, and G. Karypis, Introduction to Parallel Computing, Design and Analysis of Algorithms. Redwood City, CA: Benjamin/Cummings Publishing Company Inc., 1994.
29. M. Lewis, V. Jooste, and A. A. de Gasparis, "Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery," IEEE Trans. Geosci. and Remote Sensing, vol. 39, no. 7, pp. 1471-1479, Jul. 2001.
30. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters. San Diego, CA: Academic, 1994.
31. J. F. Mustard, L. Li, and G. He, "Nonlinear spectral mixture modeling of lunar multispectral data: Implications for lateral transport," J. Geophys. Res., vol. 103, no. E8, pp. 19419-19425, Aug. 1998.
32. A. Plaza, P. Matrinez, R. Perez, and J. Plaza, "A quantitative and comparitive analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.
33. D. N. Rassokhin and D. K. Agrafiotis, "A modified update rule for stochastic proximity embedding," J. Molecul. Graph. Modeling, vol. 22, pp. 133-140, 2003.
34. D. A. Roberts, M. O. Smith, and J. B. Adams, "Green vegetation, non-photosynthetic vegetation, and soils in AVIRIS data," Remote Sens. Environ., vol. 44, pp. 255-269, 1993.
35. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.
36. L. K. Saul and S. T. Roweis. (2001, Jan.) An introduction to locally linear embedding. [Online]. Available: http://www.cs.toronto.edu/~roweis/lle/ publications.html.
37. S. R. Sandmeier, E. M. Middleton, D. W. Deering, and W. Qin, "The potential of hyperspectral bidirectional reflectance distribution function data for grass canopy characterization," J. Geophys. Res., vol. 104, no. D8, pp. 9547-9560, Apr. 1999.
38. R. Sedgewick, Algorithms in C++, Part 5: Graph Algorithms. Reading, MA: Addison-Wesley, 2002.
39. T. H. Cormen, L. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.
40. G. Sleijpen and H. van der Vorst. (1994) Jacobi-Davidson Methods. [Online]. Available: http://www.cs.utk.edu/~dongarra/etemplates/node136. html
41. N. Straumann, General Relativity and Relativistic Astrophysics. Berlin, Germany: Springer-Verlag, 1984.
42. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, pp. 2319-2323, Dec. 2000.
43. F. Van Der Meer, "Iterative spectral unmixing," Int. J. Remote Sens., vol. 20, no. 17, pp. 3431-3436, 1999.
44. W. Verhoef, "Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model," Remote Sens. Environ., vol. 16, pp. 125-141, 1984.
45. M. M. Verstraete and B. Pinty, "Designing optimal spectral indexes for remote sensing applications," IEEE Trans. Geosci. Remote Sens., vol. 34, no. 5, pp. 1254-1264, Sep. 1996.
46. M. E. Winter, "Fast autonomous spectral endmember determination in hyperspectral data," in Proc. 13th Int. Conf. Applied Geologic Remote Sensing, vol. II, Vancouver, BC, Canada, 1999, pp. 337-344.