Spatially constrained multiple endmember spectral mixture analysis for quantifying subpixel urban impervious surfaces

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 6, 2014, Pages 1976-1984

  Wu, Changshan ^a,b   Deng, Chengbin ^c   Jia, Xiuping ^d  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^c BINGHAMTON UNIVERSITY   (United States)

^d UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Endmember extraction; endmember variability; multiple endmember spectral mixture analysis (MESMA); urban land cover; V I S model

Indexed keywords

CLUSTER ANALYSIS; EXTRACTION;

ENDMEMBER EXTRACTION; ENDMEMBER VARIABILITIES; ENHANCED THEMATIC MAPPERS; K-MEANS CLUSTERING METHOD; MULTIPLE ENDMEMBER SPECTRAL MIXTURE ANALYSIS; REMOTE SENSING IMAGERY; URBAN IMPERVIOUS SURFACES; URBAN LAND COVER;

MIXTURES;

IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGERY; LAND COVER; PIXEL; REMOTE SENSING; SPECTRAL ANALYSIS; URBAN AREA;

EID: 84905901018     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2318018     Document Type: Article

References (52)

1. G. Foody and D. Cox, "Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions," Remote Sens., vol. 15, pp. 619-631, 1994. (Pubitemid 24404006)
2. J. B. Adams, M. O. Smith, and P. E. Johnson, "Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site," J. Geophys. Res., vol. 91, pp. 8098-8112, 1986.
3. J. B. Adams, D. E. Sabol, V. Kapos, R. Almeida, D. A. Roberts, M. O. Smith et al., "Classification of multispectral images based on fractions of endmembers-Application to land-cover change in the Brazilian Amazon," Remote Sens. Environ., vol. 52, pp. 137-154, May 1995. (Pubitemid 26438386)
4. D. A. Roberts, M. Gardner, R. Church, S. Ustin, G. Scheer, and R. Green, "Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models," Remote Sens. Environ., vol. 65, pp. 267-279, 1998. (Pubitemid 29360703)
5. M. O. Smith, S. L. Ustin, J. B. Adams, and A. R. Gillespie, "Vegetation in deserts: I. A regional measure of abundance from multispectral images," Remote Sens. Environ., vol. 31, pp. 1-26, 1990.
6. S. Tompkins, J. F. Mustard, C. M. Pieters, and D. W. Forsyth, "Optimization of endmembers for spectral mixture analysis," Remote Sens. Environ., vol. 59, pp. 472-489, 1997.
7. G. P. Asner and D. B. Lobell, "A biogeophysical approach for automated SWIR unmixing of soils and vegetation," Remote Sens. Environ., vol. 74, pp. 99-112, 2000.
8. R. L. Powell, D. A. Roberts, P. E. Dennison, and L. L. Hess, "Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil," Remote Sens. Environ., vol. 106, pp. 253-267, 2007. (Pubitemid 46027178)
9. B. Somers, G. P. Asner, L. Tits, and P. Coppin, "Endmember variability in spectral mixture analysis: A review," Remote Sens. Environ., vol. 115, pp. 1603-1616, 2011.
10. 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. (Pubitemid 30594771)
11. B. Somers, S. Delalieux, J. Stuckens, W. Verstraeten, and P. Coppin, "A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems," Int. J. Remote Sens., vol. 30, pp. 139-147, 2009.
12. A. Bateson and B. Curtiss, "A method for manual endmember selection and spectral unmixing," Remote Sens. Environ., vol. 55, pp. 229-243, 1996.
13. C. Wu, "Normalized spectral mixture analysis for monitoring urban composition using imagery," Remote Sens. Environ., vol. 93, pp. 480-492, 2004. (Pubitemid 39444375)
14. C. Wu, "Quantifying high?resolution impervious surfaces using spectral mixture analysis," Int. J. Remote Sens., vol. 30, pp. 2915-2932, 2009.
15. J. Zhang, B. Rivard, and A. Sánchez-Azofeifa, "Spectral unmixing of normalized reflectance data for the deconvolution of lichen and rock mixtures," Remote Sens. Environ., vol. 95, pp. 57-66, 2005. (Pubitemid 40240807)
16. J. Zhang, B. Rivard, and A. Sanchez-Azofeifa, "Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 9, pp. 1934-1940, Sep. 2004.
17. P. Debba, E. J. M. Carranza, F. D. van der Meer, and A. Stein, "Abundance estimation of spectrally similar minerals by using derivative spectra in simulated annealing," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3649-3658, Dec. 2006. (Pubitemid 44955564)
18. B. Somers, S. Delalieux, W. Verstraeten, J. Van Aardt, G. Albrigo, and P. Coppin, "An automated waveband selection technique for optimized hyperspectral mixture analysis," Int. J. Remote Sens., vol. 31, pp. 5549-5568, 2010.
19. C.-I. Chang and B. Ji, "Weighted abundance-constrained linear spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 378-388, Feb. 2006. (Pubitemid 43146057)
20. B. Somers, S. Delalieux, W. W. Verstraeten, J. Verbesselt, S. Lhermitte, and P. Coppin, "Magnitude-and shape-related feature integration in hyperspectral mixture analysis to monitor weeds in citrus orchards," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3630-3642, Nov. 2009.
21. L. Wang and X. Jia, "Integration of soft and hard classification using extended Support Vector Machine," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 543-547, Jul. 2009.
22. J. Franke, D. A. Roberts, K. Halligan, and G. Menz, "Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments," Remote Sens. Environ., vol. 113, pp. 1712-1723, 2009.
23. D. A. Roberts, D. A. Quattrochi, G. C. Hulley, S. J. Hook, and R. O. Green, "Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission," Remote Sens. Environ., vol. 117, pp. 83-101, 2012.
24. C. Deng and C. Wu, "BCI: A biophysical composition index for remote sensing of urban environments," Remote Sens. Environ., vol. 127, pp. 247-259, 2012.
25. P. E. Dennison and D. A. Roberts, "Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE," Remote Sens. Environ., vol. 87, pp. 123-135, 2003. (Pubitemid 39661164)
26. T. Rashed, J. R. Weeks, D. Roberts, J. Rogan, and R. Powell, "Measuring the physical composition of urban morphology using multiple endmember spectral mixture models," Photogramm. Eng. Remote Sens., vol. 69, pp. 1011-1020, 2003. (Pubitemid 37078321)
27. T. C. Eckmann, D. A. Roberts, and C. J. Still, "Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS," Remote Sens. Environ., vol. 112, pp. 3773-3783, 2008.
28. T. Eckmann, D. Roberts, and C. Still, "Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis," Int. J. Remote Sens., vol. 30, pp. 5851-5864, 2009.
29. D. A. Roberts, P. E. Dennison, M. E. Gardner, Y. Hetzel, S. L. Ustin, and C. T. Lee, "Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer," IEEE Trans.Geosci. Remote Sens., vol. 41, no. 6, pp. 1297-1310, Jun. 2003.
30. A. Plaza, P. Martinez, 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. (Pubitemid 35458399)
31. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," in Proc. SPIE's Int. Symp. Opt. Sci. Eng. Instrum., 1999, pp. 266-275.
32. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
33. J. M. Nascimento and J. M. Dias, "Vertex component analysis: A fast algorithmto unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, 2005. (Pubitemid 40476033)
34. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York, NY, USA: Springer-Verlag, 2003.
35. R. Neville, K. Staenz, T. Szeredi, J. Lefebvre, and P. Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," in Proc. 21st Can. Symp. Remote Sens., 1999, pp. 21-24.
36. B. Somers, M. Zortea, A. Plaza, and G. P. Asner, "Automated extraction of image-based endmember bundles for improved spectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 396-408, Apr. 2012.
37. J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A k-means clustering algorithm," J. Roy. Stat. Soc. Ser. C, vol. 28, pp. 100-108, 1979.
38. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.
39. C. Deng and C. Wu, "A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution," Remote Sens. Environ., vol. 133, pp. 62-70, 2013.
40. W. R. Tobler, "A computer movie simulating urban growth in the Detroit region," Econ. Geogr., vol. 46, pp. 234-240, 1970.
41. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, D. Qian, P. Gader et al., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 354-379, Apr. 2012.
42. K. Canham, A. Schlamm, A. Ziemann, B. Basener, and D. Messinger, "Spatially adaptive hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4248-4262, Nov. 2011.
43. M.A. Goenaga,M.C. Torres-Madronero,M.Velez-Reyes, S. J. VanBloem, and J. D. Chinea, "Unmixing analysis of a time series of hyperion images over the Guánica dry forest in puerto rico," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 329-338, Apr. 2013.
44. M. A. Goenaga and M. Velez-Reyes, "Comparing quadtree region partitioning metrics for hyperspectral unmixing," in Proc. SPIE Algorithms Technol. Multispectral Hyperspectral Ultraspectral Imagery XIX, May 29, 2013, vol. 8743, no. 87430Z, pp. 1-12.
45. X. Chen, J. Chen, X. Jia, B. Somers, J. Wu, and P. Coppin, "A quantitative analysis of virtual endmembers' increased impact on the collinearity effect in spectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 8, pp. 2945-2956, Aug. 2011.
46. S. Roessner, K. Segl, U. Heiden, and H. Kaufmann, "Automated differentiation of urban surfaces based on airborne hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1525-1532, Jul. 2001. (Pubitemid 32732664)
47. D. M. Rogge, B. Rivard, J. Zhang, A. Sanchez, J. Harris, and J. Feng, "Integration of spatial-spectral information for the improved extraction of endmembers," Remote Sens. Environ., vol. 110, no. 3, pp. 287-303, Oct. 2007. (Pubitemid 47285207)
48. A. Zare and P. Gader, "PCE: Piece-wise convex endmember detection," IEEE Trans.Geosci. Remote Sens., vol. 48, no. 6, pp. 2620-2632, Jun. 2010.
49. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Boca Raton, London, New York, Washington, D.C.: Chapman and Hall/CRC, 1984.
50. J. Rouse, R. Haas, J. Schell, D. Deering, and J. Harlan, Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. College Station, TX, USA: Remote Sensing Center, Texas A&M University, 1974.
51. C. Small, "Estimation of urban vegetation abundance by spectral mixture analysis," Int. J. Remote Sens., vol. 22, pp. 1305-1334, 2001. (Pubitemid 32418346)
52. C. Small, "A global analysis of urban reflectance," Int. J. Remote Sens., vol. 26, pp. 661-681, 2005. (Pubitemid 40210547)