Collinearity and orthogonality of endmembers in linear spectral unmixing

International Journal of Applied Earth Observation and Geoinformation

Volumn 18, Issue 1, 2012, Pages 491-503

  Van der Meera, Freek D ^a   Jia, Xiuping ^b  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Collinearity orthogonality endmembers linear spectral unmixing

Indexed keywords

IMAGE CLASSIFICATION; LINEARITY; MATRIX; PIXEL; SPECTRAL ANALYSIS;

EID: 84864498568     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2011.10.004     Document Type: Article

References (71)

1. Adams, J.B., Sabol, D.E., Kapos, V., Almeida Filho, R., Roberts, D.A., Smith, M.O., Gillespie, A.R., 1995. Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment 52, 137-154.
2. Adams, J.B., Smith, M.O., Johnson, P.E., 1986. Spectral mixture modeling - a new analysis of rock and soil types at the Viking Lander-1 site. Journal of Geophysical Research 91, 8098-8112.
3. Adams, J.B., Smith, M.O., Gillespie, A.R., 1993. Imaging spectroscopy: interpretation based on spectral mixture analysis. In: Pieters, C.M., Englert, P. (Eds.), Remote Geochemical Analysis: Elemental and Mineralogical Composition. Cambridge University Press, New York, pp. 145-166.
4. Asner, G.P., Heidebrecht, K.B., 2002. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations. International Journal of Remote Sensing 23, 3939-3958.
5. Asner, G.P., Lobell, D.B., 2000. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sensing of Environment 74, 99-112.
6. Bateson, C.A., Asner, G.P., Wessman, C.A., 2000. Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing 38, 1083-1094.
7. Boardman, J.W., 1989. Inversion of imaging spectrometry data using singular value decomposition. In: Proceedings of Geoscience and Remote Sensing Symposium, IGARSS'89, Vancouver, British Columbia, pp. 2069-2072.
8. Chang, C.I., 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer Academic/Plenum Publishers, New York.
9. Chang, C.I., Plaza, A., 2006. A fast iterative algorithm for implementation of pixel purity index. IEEE Geoscience and Remote Sensing Letters 3, 63-67.
10. Chang, C.I., Zhao, X.L., Althouse, M.L.G., Pan, J.J., 1998. Least squares subspace projection approach to mixed pixel classification for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing 36, 898-912.
11. Chang, C.I., Wu, C.C., Lo, C.S., Chang, M.L., 2010a. Real-time simplex growing algorithms for hyperspectral endmember extraction. IEEE Transactions on Geoscience and Remote Sensing 48, 1834-1850.
12. Chang, C.I., Wu, C.C., Chen, H.M., 2010b. Random pixel purity index. IEEE Geoscience and Remote Sensing Letters 7, 324-328.
13. Chen, J., Jia, X.P., Yang, W., Matsushita, B., 2009. Generalization of subpixel analysis for hyperspectral data with flexibility in spectral similarity measures. IEEE Transactions on Geoscience and Remote Sensing 47, 2165-2171.
14. Chen, X., Chen, J., Jia, X., WU, J., 2010. Impact of collinearity on linear and nonlinear spectral mixture analysis. In: Benediktsson, J.A. (Ed.), 2nd Workshop on Hyperspectral Image and Signal Processing (WHISPERS): Evolution in remote sensing. IEEE, Reykjavik, Iceland.
15. Clark, R.N., 1983. Spectral properties of mixtures of montmorillonite and dark carbon grains: implications for remote sensing minerals containing chemically and physically absorbed water. Journal of Geophysical Research 88, 10635-10644.
16. Cloutis, E.A., 1996. Hyperspectral geological remote sensing: evaluation of analytical techniques. International Journal of Remote Sensing 17, 2215-2242.
17. de Jong, S.M., Paracchini, M.L., Bertolo, F., Folving, S., Megier, J., de Roo, A.P.J., 1999. Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. Catena 37, 291-308.
18. Drake, N.A., Mackin, S., Settle, J.J., 1999. Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sensing of Environment 68, 12-25.
19. Foody, G.M., Cox, D.P., 1994. Sub-pixel land-cover composition estimation using linear mixture model and fuzzy membership functions. International Journal of Remote Sensing 15, 619-631.
20. Fox, J., Monette, G., 1992. Generalized collinearity diagnostics. Journal of the American Statistical Association 87, 178-183.
21. Graham, M.H., 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809-2815.
22. Green, A.A., Berman, M., Switzer, P., Craig, M.D., 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing 26, 65-74.
23. Harsanyi, J.C., Chang, C.I., 1994. Hyperspectral image classification and dimensionality reduction - an orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing 32, 779-785.
24. Hecker, C., van der Meijde, M., van der Meer, F.D., 2010. Thermal infrared spectroscopy on feldspars - successes, limitations and their implications for remote sensing. Earth-Science Reviews 103, 60-70.
25. Hedley, J.D., Mumby, P.J., 2003. A remote sensing method for resolving depth and subpixel composition of aquatic benthos. Limnology and Oceanography 48, 480-488.
26. Hostert, P., Diermayer, E., Damm, A., Schiefer, S., 2005. Spectral Unmixing Based on Image and Reference Endmembers for Urban Change Analysis. Millpress Science Publishers, Rotterdam.
27. Hunt, G.R., 1977. Spectral signatures of particulate minerals in the visible and nearinfrared. Geophysics 42, 501-513.
28. Ichoku, C., Karnieli, A., 1996. A review of mixture modeling techniques for sub-pixel land cover estimation. Remote sensing reviews 13, 161-186.
29. Ifarraguerri, A., Chang, C.I., 1999. Multispectral and hyperspectral image analysis with convex cones. IEEE Transactions on Geoscience and Remote Sensing 37, 756-770.
30. Johnson, P.E., Smith, M.O., Taylor-George, S., Adams, J.B., 1983. A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures. Journal of Geophysical Research 88, 3557-3561.
31. Ju, J.C., Kolaczyk, E.D., Gopal, S., 2003. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment 84, 550-560.
32. Keshava, N., Mustard, J.F., 2002. Spectral unmixing. IEEE Signal Processing Magazine 19, 44-57.
33. Kruse, F.A., Lefkoff, A.B., Dietz, J.B., 1993. Expert system-based mineral mapping in Northern Death-Valley, California Nevada, using the airborne visible infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment 44, 309-336.
34. Lelong, C.C.D., Pinet, P.C., Poilve, H., 1998. Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sensing of Environment 66, 179-191.
35. Lu, D., Batistella, M., Moran, E., Mausel, P., 2004. Application of spectral mixture analysis to Amazonian land-use and land-cover classification. International Journal of Remote Sensing 25, 5345-5358.
36. Martin, G., Plaza, A., 2011. Region-based spatial preprocessing for endmember extraction and spectral unmixing. IEEE Geoscience and Remote Sensing Letters 8, 745-749.
37. Mei, S.H., He, M.Y., Wang, Z.Y., Feng, D.G., 2010. Spatial purity based endmember extraction for spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing 48, 3434-3445.
38. Metternicht, G.I., Fermont, A., 1998. Estimating erosion surface features by linear mixture modeling. Remote Sensing of Environment 64, 254-265.
39. Nascimento, J.M.P., Dias, J.M.B., 2005. Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43, 898-910.
40. Neville, R.A., Levesque, J., Staenz, K., Nadeau, C., Hauff, P., Borstad, G.A., 2003. Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS. Canadian Journal of Remote Sensing 29, 99-110.
41. O'Brien, R.M., 2007. A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 41, 673-690.
42. Okin, G.S., Roberts, D.A., Murray, B., Okin, W.J., 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment 77, 212-225.
43. Plaza, A., Martinez, P., Perez, R., Plaza, J., 2002. Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing 40, 2025-2041.
44. Price, J.C., 1994. How unique are spectral signatures? Remote Sensing of Environment 49, 181-186.
45. Roberts, D.A., Smith, M.O., Adams, J.B., 1993. Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment 44, 255-269.
46. Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G., R.O., G., 1998. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 267-279.
47. Roessner, S., Segl, K., Heiden, U., Kaufmann, H., 2001. Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39, 1525-1532.
48. Rogge, D.M., Rivard, B., Zhang, J., Feng, J., 2006. Iterative spectral unmixing for optimizing per-pixel endmember sets. IEEE Transactions on Geoscience and Remote Sensing 44, 3725-3736.
49. Rogge, D.M., Rivard, B., Zhang, J., Sanchez, A., Harris, J., Feng, J., 2007. Integration of spatial-spectral information for the improved extraction of endmembers. Remote Sensing of Environment 110, 287-303.
50. Sabol, D.E., Adams, J.B., Smith, M.O., 1992. Quantitative subpixel spectral detection of targets in multispectral images. Journal of Geophysical Research 97, 2659-2672.
51. Segl, K., Roessner, S., Heiden, U., Kaufmann, H., 2003. Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 58, 99-112.
52. Settle, J.J., Drake, N.A., 1993. Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing 14, 1159-1177.
53. Shao, Y., Lunetta, R.S., 2011. Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian Great Lakes Basin using MODIS time-series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, 336-347.
54. Shi, L., Wang, X.R., 1999. Local influence in ridge regression. Computational Statistics and Data Analysis 31, 341-353.
55. Singer, R.B., 1981. Near-infrared spectral reflectance of mineral mixtures: systematic combinations of pyroxenes, olivine, and iron oxides. Journal of Geophysical Research 86, 7967-7982.
56. Singer, R.B., McCord, T.B., 1979. Mars: large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance. In: Proceedings of the 10th Lunar and Planetary Science Conference, Houston, U.S, pp. 1835-1848.
57. Somers, B., Delalieux, S., Verstraeten, W.W., van Aardt, J.A.N., Albrigo, G.L., Coppin, P., 2010. An automated waveband selection technique for optimized hyperspectral mixture analysis. International Journal of Remote Sensing 31, 5549-5568.
58. Somers, B., Asner, G.P., Tits, L., Coppin, P., 2011. Endmember variability in spectral mixture analysis: a review. Remote Sensing of Environment 115, 1603-1616.
59. Thof, I., Fraser, R.H., 2007. Mapping northern land cover fractions using Landsat ETM. Remote Sensing of Environment 107, 496-509.
60. Tompkins, S., Mustard, J.F., Pieters, C.M., Forsyth, D.W., 1997. Optimization of endmembers for spectral mixture analysis. Remote Sensing of Environment 59, 472-489.
61. Van der Meer, F., 1999. Iterative spectral unmixing (ISU). International Journal of Remote Sensing 20, 3431-3436.
62. Van der Meer, F., 2006a. Indicator kriging applied to absorption band analysis in hyperspectral imagery: a case study from the Rodalquilar epithermal gold mining area, SE Spain. International Journal of Applied Earth Observation and Geoinformation 8, 61-72.
63. Van der Meer, F., 2006b. The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation 8, 3-17.
64. Van der Meer, F., De Jong, S.M., 2000a. Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of endmembers. International Journal of Remote Sensing 21, 2781-2797.
65. Van der Meer, F., De Jong, S., 2000b. Imaging Spectrometry: Basic Principles and Prospective Applications. Kluwer academic publishers, Dordrecht.
66. Vane, G., Green, R.O., Chrien, T.G., Enmark, H.T., Hansen, E.G., Porter, W.M., 1993. The airborne visible infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment 44, 127-143.
67. Weng, Q.H., Lu, D.S., 2008. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. International Journal of Applied Earth Observation and Geoinformation 10, 68-83.
68. Westerhuis, J.A., de Jong, S., Smilde, A.K., 2001. Direct orthogonal signal correction, chemometrics intell. Laboratory Systems 56, 13-25.
69. Winter, M.E., 1999. N-FINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data. In: Descour, M.R., Shen, S.S. (Eds.), Imaging Spectrometry V. Spie-Int Soc Optical Engineering, Bellingham, pp. 266-275.
70. Wu, C.C., Chang, C.I., Ren, H.S., Chang, Y.L., 2009. IEEE, real-time processing of simplex growing algorithm. In: 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 1-5. IEEE, New York, pp. 3645-3648.
71. Zortea, M., Plaza, A., 2009. A quantitative and comparative analysis of different implementations of N-FINDR: a fast endmember extraction algorithm. IEEE Geoscience and Remote Sensing Letters 6, 787-791.