An endmember optimization approach for linear spectral unmixing of fine-scale urban imagery

International Journal of Applied Earth Observation and Geoinformation

Volumn 27, Issue PARTB, 2014, Pages 137-146

  Yang, Jian ^a,b   He, Yuhong ^a   Oguchi, Takashi ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

Endmember optimization; High spatial resolution; Linear spectral unmixing; SESMA and MESMA; Spectral mixing space

Indexed keywords

IMAGERY; OPTIMIZATION; SPATIAL DISTRIBUTION; SPATIAL RESOLUTION; SPECTRAL ANALYSIS; URBAN AREA;

EID: 84897563622     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2013.09.013     Document Type: Article

References (46)

1. Bannari, A., Pacheco, A., Staenz, K., McNairn, H., Omari, K., 2006. Estimating and map-ping crop residues cover on agricultural lands using hyperspectral and IKONOSdata. Remote Sensing of Environment 104, 447-459.
2. Collado, A.D., Chuvieco, E., Camarasa, A., 2002. Satellite remote sensing analysis tomonitor desertification processes in the crop-rangeland boundary of Argentina.Journal of Arid Environments 52, 121-133.
3. De Asis, A., Omasa, K., Oki, K., Shimizu, Y., 2008. Accuracy and applicability of linearspectral unmixing in delineating potential erosion areas in tropical watersheds.International Journal of Remote Sensing 29, 4151-4171.
4. DeFries, R., Hansen, M., Townshend, J., 2000. Global continuous fields of vegetationcharacteristics: a linear mixture model applied to multi-year 8 km AVHRR data.International Journal of Remote Sensing 21, 1389-1414.
5. Deng, C., Wu, C., 2013. A spatially adaptive spectral mixture analysis for mappingsubpixel urban impervious surface distribution. Remote Sensing of Environment133, 62-70.
6. Deng, Y., Fan, F., Chen, R., 2012. Extraction and analysis of impervious surfacesbased on a spectral un-mixing method using Pearl River Delta of China LandsatTM/ETM + imagery from 1998 to 2008. Sensors (Basel) 12, 1846-1862.
7. Dennison, P.E., Halligan, K.Q., Roberts, D.A., 2004. A comparison of error metricsand constraints for multiple endmember spectral mixture analysis and spectralangle mapper. Remote Sensing of Environment 93, 359-367.
8. Dennison, P.E., Roberts, D.A., 2003. Endmember selection for multiple endmemberspectral mixture analysis using endmember average RMSE. Remote Sensing ofEnvironment 87, 123-135.
9. Fernández-Manso, A., Quintano, C., Roberts, D., 2012. Evaluation of potential of mul-tiple endmember spectral mixture analysis (MESMA) for surface coal miningaffected area mapping in different world forest ecosystems. Remote Sensing ofEnvironment 127, 181-193.
10. Franke, J., Roberts, D.A., Halligan, K., Menz, G., 2009. Hierarchical multiple end-member spectral mixture analysis (MESMA) of hyperspectral imagery for urbanenvironments. Remote Sensing of Environment 113, 1712-1723.
11. Hajja, M., Walker, P., 2002. The measure of solid angles in n-dimensional Euclideanspace. International Journal of Mathematical Education in Science and Technol-ogy 33, 725-729.
12. Hu, X., Weng, Q., 2009. Estimating impervious surfaces from medium spatial resolu-tion imagery using the self-organizing map and multi-layer perceptron neuralnetworks. Remote Sensing of Environment 113, 2089-2102.
13. Keshava, N., 2003. A survey of spectral unmixing algorithms. Lincoln LaboratoryJournal 14, 55-78.
14. Laben, C.A., Brower, B.V., 2000. Process for enhancing the spatial resolution of multi-spectral imagery using pansharpening. U.S. Patent No. 6,011,875, Eastman KodakCompany.
15. Liu, T., Yang, X., 2013. Mapping vegetation in an urban area with stratified classifi-cation and multiple endmember spectral mixture analysis. Remote Sensing ofEnvironment 133, 251-264.
16. McGwire, K., Minor, T., Fenstermaker, L., 2000. Hyperspectral mixture modeling forquantifying sparse vegetation cover in arid environments. Remote Sensing ofEnvironment 72, 360-374.
17. Michishita, R., Jiang, Z., Xu, B., 2012. Monitoring two decades of urbanization inthe Poyang Lake area, China through spectral unmixing. Remote Sensing ofEnvironment 117, 3-18.
18. Nichol, J., Wong, M., 2007. Remote sensing of urban vegetation life form by spec-tral mixture analysis of high-resolution IKONOS satellite images. InternationalJournal of Remote Sensing 28, 985-1000.
19. Nichol, J.E., Wong, M.S., Corlett, R., Nichol, D.W., 2010. Assessing avian habitat frag-mentation in urban areas of Hong Kong (Kowloon) at high spatial resolutionusing spectral unmixing. Landscape and Urban Planning 95, 54-60.
20. Powell, R.L., Roberts, D.A., 2010. Characterizing Urban Land-Cover Change in Rondô-nia, Brazil: 1985 to 2000. Journal of Latin American Geography 9, 183-211.
21. Powell, R.L., Roberts, D.A., Dennison, P.E., Hess, L.L., 2007. Sub-pixel mapping of urbanland cover using multiple endmember spectral mixture analysis: Manaus. Brazil.Remote Sensing of Environment 106, 253-267.
22. Puyou-Lascassies, P., Flouzat, G., Gay, M., Vignolles, C., 1994. Validation of the useof multiple linear regression as a tool for unmixing coarse spatial resolutionimages. Remote Sensing of Environment 49, 155-166.
23. Quintano, C., Fernández-Manso, A., Shimabukuro, Y.E., Pereira, G., 2012. Spectralunmixing. International Journal of Remote Sensing 33, 5307-5340.
24. Ridd, M.K., 1995. Exploring a VIS (vegetation-impervious surface-soil) model forurban ecosystem analysis through remote sensing: comparative anatomy forcities. International Journal of Remote Sensing 16, 2165-2185.
25. Roberts, D., Halligan, K., Dennison, P., 2007. VIPER Tools User Manual. Univ. of Calif.,Santa Barbara, CA.
26. Roberts, D.A., Dennison, P.E., Gardner, M.E., Hetzel, Y., Ustin, S.L., Lee,C.T., 2003. Evaluation of the potential of Hyperion for fire dangerassessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer. IEEE Transactions on Geoscience and Remote Sensing 41,1297-1310.
27. Rogge, D.M., Rivard, B., Zhang, J., Feng, J., 2006. Iterative spectral unmixing for optimizing per-pixel endmember sets. IEEE Transactions on Geoscience and RemoteSensing 44, 3725-3736.
28. Shimabukuro, Y.E., Smith, J.A., 1991. The least-squares mixing models to generatefraction images derived from remote sensing multispectral data. IEEE Transac-tions on Geoscience and Remote Sensing 29, 16-20.
29. Shrestha, D., Margate, D., Meer, F.v.d., Anh, H., 2005. Analysis and classification ofhyperspectral data for mapping land degradation: An application in southernSpain. International Journal of Applied Earth Observation and Geoinformation7, 85-96.
30. Sirguey, P., Mathieu, R., Arnaud, Y., 2009. Subpixel monitoring of the seasonal snowcover with MODIS at 250 m spatial resolution in the Southern Alps of NewZealand: Methodology and accuracy assessment. Remote Sensing of Environment 113, 160-181.
31. Small, C., 2001. Estimation of urban vegetation abundance by spectral mixture anal-ysis. International Journal of Remote Sensing 22, 1305-1334.
32. Small, C., 2003. High spatial resolution spectral mixture analysis of urban reflectance.Remote Sensing of Environment 88, 170-186.
33. Small, C., 2005. A global analysis of urban reflectance. International Journal ofRemote Sensing 26, 661-681.
34. Small, C., Lu, J.W., 2006. Estimation and vicarious validation of urban vegetationabundance by spectral mixture analysis. Remote Sensing of Environment 100,441-456.
35. Somers, B., Asner, G.P., Tits, L., Coppin, P., 2011. Endmember variability in spectralmixture analysis: a review. Remote Sensing of Environment 115, 1603-1616.
36. Song, C., 2005. Spectral mixture analysis for subpixel vegetation fractions inthe urban environment: How to incorporate endmember variability? RemoteSensing of Environment 95, 248-263.
37. Thorp, K., French, A., Rango, A., 2013. Effect of image spatial and spectral character-istics on mapping semi-arid rangeland vegetation using multiple endmemberspectral mixture analysis (MESMA). Remote Sensing of Environment 132,120-130.
38. Tompkins, S., Mustard, J.F., Pieters, C.M., Forsyth, D.W., 1997. Optimization of end-members for spectral mixture analysis. Remote Sensing of Environment 59,472-489.
39. Van de Voorde, T., De Roeck, T., Canters, F., 2009. A comparison of two spectralmixture modelling approaches for impervious surface mapping in urban areas.International Journal of Remote Sensing 30, 4785-4806.
40. Van Der Meer, F., De Jong, S.M., 2000. Improving the results of spectral unmixingof Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members. International Journal of Remote Sensing 21, 2781-2797.
41. Van der Meer, F.D., Jia, X., 2012. Collinearity and orthogonality of endmembers inlinear spectral unmixing. International Journal of Applied Earth Observation andGeoinformation 18, 491-503.
42. Veraverbeke, S., Somers, B., Gitas, I., Katagis, T., Polychronaki, A., Goossens, R.,2012. Spectral mixture analysis to assess post-fire vegetation regeneration usingLandsat Thematic Mapper imagery: Accounting for soil brightness variation.International Journal of Applied Earth Observation and Geoinformation 14, 1-11.
43. Vikhamar, D., Solberg, R., 2003. Subpixel mapping of snow cover in forests by opticalremote sensing. Remote Sensing of Environment 84, 69-82.
44. Weng, Q., Hu, X., Liu, H., 2009. Estimating impervious surfaces using linear spec-tral mixture analysis with multitemporal ASTER images. International Journalof Remote Sensing 30, 4807-4830.
45. Wu, C., Murray, A.T., 2003. Estimating impervious surface distribution by spectralmixture analysis. Remote Sensing of Environment 84, 493-505.
46. Zhang, Q., Wang, J., Peng, X., Gong, P., Shi, P., 2002. Urban built-up land change detec-tion with road density and spectral information from multi-temporal LandsatTM data. International Journal of Remote Sensing 23, 3057-3078.