Support Vector machine-based endmember extraction

IEEE Transactions on Geoscience and Remote Sensing

Volumn 47, Issue 3, 2009, Pages 771-791

  Filippi, Anthony M ^a   Archibald, Rick ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

Endmember extraction; Hyperspectral imaging; Remote sensing; Support vector machines (SVMs)

Indexed keywords

COST REDUCTION; GEARS; IMAGE RETRIEVAL; KNOWLEDGE REPRESENTATION; MULTILAYER NEURAL NETWORKS; REMOTE SENSING; VECTORS;

COMPUTATIONAL COSTS; COMPUTATIONAL SPEED; DATA DIMENSIONALITIES; DATA SETS; END-MEMBERS; ENDMEMBER EXTRACTION; ENDMEMBER EXTRACTION ALGORITHMS; HIGH-DIMENSIONAL DATUM; HYPER-SPECTRAL DATUM; HYPERSPECTRAL IMAGING; MULTIPLE CLUSTERS; MULTIPLE DISTRIBUTIONS; PRIOR KNOWLEDGE; ROBUST TOLERANCES; SUPPORT VECTOR MACHINES (SVMS);

SUPPORT VECTOR MACHINES;

ALGORITHM; DATA PROCESSING; DATA SET; IMAGING METHOD; REMOTE SENSING;

EID: 61349196839     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2008.2004708     Document Type: Article

References (60)

1. J. R. Jensen and A. M. Filippi, "Thematic information extraction: Hy-
2. perspectral image analysis," in Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed. J. R. Jensen, Ed. Upper Saddle River, NJ: Prentice-Hall, 2005, pp. 431-465.
3. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer, 2003.
4. D. M. Rogge, B. Rivard, J. Zhang, and J. Feng, "Iterative spectral unmixing for optimizing per-pixel endmember sets," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3725-3736, Dec. 2006.
5. J. J. Settle and N. A. Drake, "Linear mixing and the estimation of ground cover proportions," Int. J. Remote Sens., vol. 14, no. 6, pp. 1159-1177, Apr. 1993.
6. R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 2nd ed. San Diego, CA: Academic, 1997.
7. J. Gruninger, A. J. Ratkowski, and M. L. Hoke, "The sequential maximum angle convex cone (SMACC) endmember model," in Proc. SPIE-Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, S. S. Shen and P. E. Lewis, Eds. Bellingham, WA: SPIE, 2004, vol. 5425, pp. 1-14.
8. C. A. Bateson, G. P. Asner, and C. A. Wessman, "Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 1083-1094, Mar. 2000.
9. J. W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Proc. Summaries JPL Airborne Earth Sci. Workshop, Pasadena, CA, 1995, pp. 23-26.
10. ENVI User's Guide, RSI (Research Systems, Inc.), Boulder, CO, 2004. ENVI Vers. 4.1.
11. A. Bateson and B. Curtiss, "A method for manual endmember selection and spectral unmixing," Remote Sens. Environ., vol. 55, no. 3, pp. 229-243, Mar. 1996.
12. C.-I. Chang and A. Plaza, "A fast iterative algorithm for implementation of pixel purity index," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 63-67, Jan. 2006.
13. J. H. Bowles, P. J. Palmadesso, J. A. Antoniades, M. M. Baumback, and L. J. Rickard, "Use of filter vectors in hyperspectral data analysis," in Proc. SPIE-Infrared Spaceborne Remote Sensing III, 1995, vol. 2553, pp. 148-157.
14. J. H. Bowles, M. Daniel, J. M. Grossman, J. A. Antoniades, M. M. Baumback, and P. J. Palmadesso, "Comparison of output from ORASIS and pixel purity calculations," in Proc. SPIE-Imaging Spectroscopy IV, 1998, vol. 3438, pp. 148-156.
15. A. Ifarraguerri and C.-I. Chang, "Multispectral and hyperspectral image analysis with convex cones," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 756-770, Mar. 1999.
16. R. A. Neville, K. Staenz, T. Szeredi, J. Lefebvre, and P. Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," in Proc. 4th Int. Airborne Remote Sens. Conf. Exhib./21st Can. Symp. Remote Sens., Ottawa, ON, Canada, Jun. 21-24, 1999, vol. II, pp. 891-896.
17. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Proc. SPIE-Imaging Spectrometry V, M. R. Descour and S. S. Shen, Eds, 1999, vol. 3753, pp. 266-275.
18. M. E. Winter, "Fast autonomous spectral end-member determination in hyperspectral data," in Proc. 13th Int. Conf. Appl. Geologic Remote Sens., Vancouver, BC, Canada, 1999, vol. II, pp. 337-344.
19. A. Plaza, P. Martínez, R. Pérez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 2025-2041, Sep. 2002.
20. M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. F. Huntington, "ICE: A statistical approach to identifying endmembers in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10, pp. 2085-2095, Oct. 2004.
21. J. M. P. Nascimento and J. M. B. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005.
22. M. Graña, C. Hernandez, and J. Gallego, "A single individual evolutionary strategy for endmember search in hyperspectral images," Inf. Sci., vol. 161, no. 3/4, pp. 181-197, Apr. 2004.
23. K. Staenz, T. Szeredi, and J. Schwarz, "ISDAS-A system for processing/analyzing hyperspectral data," Can. J. Remote Sens., vol. 24, no. 2, pp. 99-113, 1998.
24. J. Chanussot, J. A. Benediktsson, and M. Fauvel, "Classification of remote sensing images from urban areas using a fuzzy possibilistic model," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 40-44, Jan. 2006.
25. B. E. Boser, I. M. Guyon, and V N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Comput. Learning Theory, 1992, pp. 144-152.
26. V N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
27. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Evaluation of kernels for multiclass classification of hyperspectral remote sensing data," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Toulouse, France, May 14-19, 2006, pp. II-813 II-816.
28. G. Camps-Vails et al., "Robust support vector method for hyperspectral data classification and knowledge discovery," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, pp. 1530-1542, Jul. 2004.
29. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
30. G. Camps-Vails and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
31. G. M. Foody and A. Mathur, "The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM," Remote Sens. Environ., vol. 103, no. 2, pp. 179-189, Jul. 2006.
32. M. Brown, S. R. Gunn, and H. G. Lewis, "Support vector machines for optimal classification and spectral unmixing," Ecol. Model., vol. 120, no. 2/3, pp. 167-179, Aug. 1999.
33. M. Brown, H. G. Lewis, and S. R. Gunn, "Linear mixture models and support vector machines for remote sensing," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2346-2360, Sep. 2000.
34. L. Wang, Y. Zhang, and C. Zhao, "Combination of linear support vector machines and linear spectral mixed model for spectral unmixing," in Intelligent Computing in Signal Processing and Pattern Recognition, vol. 345, D.-S. Huang, K. Li, and G. W Irwin, Eds. Berlin, Germany: Springer-Verlag, Sep. 2006, pp. 767-772.
35. A. Banerjee, P. Burlina, and J. Broadwater, "A machine learning approach for finding hyperspectral endmembers," in Proc. IEEE IGARSS, Jul. 23-27, 2007, pp. 3817-3820.
36. D. M. J. Tax and R. P. W Duin, "Data domain description using support vectors," in Proc. 7th ESANN, Bruges, Belgium, Apr. 21-23, 1999, pp. 251-256.
37. D. M. J. Tax and R. P. W. Duin, "Support vector domain description," Pattern Recognit. Lett., vol. 20, no. 11-13, pp. 1191-1199, Nov. 1999.
38. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, "Support vector clustering," J. Mach. Learn. Res., vol. 2, pp. 125-137, Dec. 2001.
39. R. B. Cattell, "The scree test for the number of factors," Multivariate Behav. Res., vol. 1, pp. 245-276, 1966.
40. C.-I. Chang, C.-C. Wu, W Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Trans. Geosci. Remote Sens., vol. 4, no. 10, pp. 2804-2819, Oct. 2006.
41. N-FINDR Visualization Package Documentation, Tech. Res. Assoc, Inc., Honolulu, HI, May 31, 2006.
42. R. N. Clark, G. A. Swayze, A. J. Gallagher, T. V V King, and W M. Calvin, "The U. S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns," U.S. Geological Survey, Denver, CO, Open File Rep. 93-592, 1993.
43. A. Plaza and C.-I. Chang, "Impact of initialization on design of endmember extraction algorithms," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3397-3407, Nov. 2006.
44. A. Goetz and V Strivastava, "Mineralogical mapping in the Cuprite mining district," in Proc. AIS Data Anal. Workshop, 1985, pp. 22-29. JPL Pub. 85-41.
45. J. P. Albers and J. H. Stewart, "Geology and mineral deposits of Esmeralda County, Nevada," Nevada Bureau Mines Geology Bull., vol. 78, pp. 1-80, 1972.
46. M. J. Abrams, R. P. Ashley, L. C. Rowan, and A. F. H. Goetz, "Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36 μm," Geology, vol. 5, no. 12, pp. 713-718, Dec. 1977.
47. M. J. Abrams, R. P. Ashley, L. C. Rowan, A. F. H. Goetz, and A. B. Kahle, "Use of imaging in the 0.46-2.36 μm spectral region for alteration mapping in the Cuprite mining district, Nevada," U.S. Geological Survey, Denver, CO, Open-File Rep. 77-585, 1977.
48. R. P. Ashley and M. J. Abrams, "Alteration mapping using multispectral images, Cuprite mining district, Esmeralda County, Nevada," U.S. Geological Survey, Denver, CO, Open-File Rep. 80-367, 1980.
49. H. Shipman and J. B. Adams, "Detectability of minerals on desert alluvial fans using reflectance spectra," J. Geophys. Res., vol. 92, pp. 10391-10402, Sep. 1987.
50. F. A. Kruse, K. S. Kierein-Young, and J. W Boardman, "Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer," Photogramm. Eng. Remote Sens., vol. 56, no. 1, pp. 83-92, Jan. 1990.
51. R. G. Resmini, M. E. Kappus, W S. Aldrich, J. C. Harsanyi, and M. Anderson, "Mineral mapping with HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A.," Int. J. Remote Sens., vol. 18, no. 7, pp. 1553-1570, May 1997.
52. R. O. Green et al., "Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)," Remote Sens. Environ., vol. 65, no. 3, pp. 227-248, Sep. 1998.
53. L. C. Rowan, S. J. Hook, M. J. Abrams, and J. C. Mars, "Mapping hydrothermally altered rocks at Cuprite, Nevada, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a new satellite-imaging system," Econ. Geol., vol. 98, no. 5, pp. 1019-1027, Aug. 2003.
54. R. O. Green, "Retrieval of reflectance from AVIRIS using a radiative transfer code," in Proc. 3rd AVIRIS Workshop, R. O. Green, Ed., Pasadena, CA, May 20-21, 1991, pp. 200-210. JPL Pub. 91-28.
55. C.-I. Chang and B. Ji, "Fisher's linear spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 8, pp. 2292-2304, Aug. 2006.
56. G. Swayze, "The hydrothermal and structural history of the Cuprite Mining District, Southwestern Nevada: An integrated geological and geophysical approach," Ph.D. dissertation, Univ. Colorado, Boulder, CO, 1997. [Online]. Available: http://speclab.cr.usgs.gov and ftp://ftpext.cr.usgs.gov/pub/cr/co/ denver/speclab/pub/cuprite/gregg. thesis.images/mineralzone.thesis.gif
57. R. N. Clark et al., "Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems," J. Geophys. Res., vol. 108, no. E12, p. 5131, 2003. DOI:10.1029/ 2002JE001847.
58. A. Plaza, D. Valencia, J. Plaza, and P. Martinez, "Commodity cluster-based parallel processing of hyperspectral imagery," J. Parallel Distrib. Comput., vol. 66, no. 3, pp. 345-358, Mar. 2006.
59. C.-I. Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608-619, Mar. 2004.
60. J. Wang and C.-I. Chang, "Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1586-1600, Jun. 2006.