Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -

  • Kang, Min Jo (Geospatial Information Research Division, Korea Research Institute Human Settlements(KRIHS)) ;
  • Mesev, Victor (Department of Geography, Florida State University) ;
  • Kim, Won Kyung (Forest Economics and Management Division, Korea Forest Research Institute)
  • Received : 2015.05.10
  • Accepted : 2015.08.18
  • Published : 2015.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.

Keywords

References

  1. Adams, J.B., M.O. Smith, and A.R. Gillespie, 1993. Imaging spectroscopy: interpretation based on spectral mixture analysis. In: Remote geochemical analysis: Elemental and mineralogical composition, Piters, C. M. and P. A. J. Englert (Editors), Cambridge University, New York, pp. 145-166.
  2. Baatz, M., U. Benz, S. Dehghani, M. Heynen, A. Holtje, P. Hofmann, L. Lingenfelder, M. Mimler, M. Sohlbach, M. Weber, and G. Willhauck, 2000. eCognition User Guide, Munchen: Definiens AG.
  3. Berman, M., A. Phatak, and A. Traylen, 2012. Some invariance properties of the minimum noise fraction transform, Chemometrics and Intelligent Laboratory Systems, 117: 189-199. https://doi.org/10.1016/j.chemolab.2012.02.005
  4. Bhaskaran, S., S. Paramananda, and M. Ramnarayan, 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data, Applied Geography, 30(4): 650-665. https://doi.org/10.1016/j.apgeog.2010.01.009
  5. Chaudhry, F., C.C. Wu, W. Liu, C.I. Chang and A. Plaza, 2006. Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery, Recent Advances in Hyperspectral Signal and Image Processing, 37/661(2): 29-62.
  6. Chabaeva, A., D.L. Civco, and J.D. Hurd, 2009. Assessment of impervious surface estimation techniques, Journal of Hydrology Engineering, 14(4SI): 377-387 https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(377)
  7. Civico, D.L., J.D. Hurd, E.H. Wilson, C.L. Arnold, and M.P. Prisloe, 2002. Quantifying and describing urbanising landscapes in the northeast United States, Photogrammetric Engineering and Remote Sensing, 68: 1083-1090.
  8. Conchedda, G., L. Durieux, and P. Mayaux, 2008. An object-based method for mapping and change analysis in mangrove ecosystems, Journal of Photogrammetry & Remote Sensing, 63: 578-589. https://doi.org/10.1016/j.isprsjprs.2008.04.002
  9. De Kok, R., T. Schneider, and U. Ammer, 1999. Objectbased classification and applications in the Alpine forest environment, International Archives of Photogrammetry and Remote Sensing, 32: 7-4-3.
  10. Dennison, P.E. and D.A. Roberts, 2003. Endmember selection for multiple endmember spectral mixture analysis using Endmember Average RMSE, Remote Sensing of Environment, 87: 123-135 https://doi.org/10.1016/S0034-4257(03)00135-4
  11. Goetz, S.J., K.W. Robb, J.S. Andrew, Z. Elizabeth, and S. Erika, 2003. IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region, Remote Sensing of Environment, 88: 195-208. https://doi.org/10.1016/j.rse.2003.07.010
  12. Hajek, F., 2006. Object-oriented classification of Ikonos satellite data for the identification of tree species composition, Journal of Forest Science, 52(4): 181-187. https://doi.org/10.17221/4500-jfs
  13. Harayama, A. and J.M. Jaquet, 2004. Multi-source object-oriented classification of land cover using very high resolution imagery and digital elevation model, UNEP Conference. Geneva, Switzerland.
  14. Hirata, Y. and T. Takahashi, 2009. Object-oriented classification and sampling rate of Landsat TM data for Forest Cover Assessment, MSU Conference. Quebec, Canada.
  15. Huang, L. and L. Ni, 2008. Object-oriented classification of high resolution satellite image for better accuracy, Proc. of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, June 25-27, Shanghai, P. R. China, pp. 211-218.
  16. Huguenin, R.L., M.H. Wang, R. Biehl, S. Stoodley, and J.N. Rogers, 2004. Automated subpixel photobathymetry and water quality mapping, Photogrammetric Engineering and Remote Sensing, 70(1): 111-123. https://doi.org/10.14358/PERS.70.1.111
  17. Hurd, J.D., D.L. Civico, M.S. Gilmore, S. Prisloe, and E.H. Wilson, 2006. Tidal Wetland Classification from Landsat Imagery Using an Integrated Pixel-based and Object-based Classification Approach, Proc. of ASPRS 2006 Annual Conference, Reno, Nevada, May 1-5, 2006.
  18. Jenerette, G.D. and W. Jianguo, 2001. Analysis and simulation of land-use changes in the central Arizona-Phoenix region, USA. Landscape Ecology, 16: 611-626. https://doi.org/10.1023/A:1013170528551
  19. Jensen, J.R. 2005. Introductory Digital Image Proc. of A Remote Sensing Perspective (3rd), Prentice Hall, Upper Saddle River.
  20. Justice, D. and F. Rubin, 2003. Developing impervious surface estimates for coastal New Hampshire, Final Report to the New Hampshire Estuaries Project, Complex Systems Research Center, Institute for the Study of Earth, Oceans and Space, Morse Hall, Univirsity of New Hampshire, Dunham, N.H.
  21. Kux, H.J.H. and E.H.G. Araujo, 2006. Multi-temporal object-oriented classification and analysis of Quickbird scenes at a metropolitan area in Brazil, 1st International Conference on Object-Based Image Analysis (OBIA). Salzbarg University, Austria.
  22. Lee, S. and R.G. Lathrop, 2005. Sub-pixel estimation of urban land-cover components with linear mixture model analysis and Landsat Thematic Mapper imagery, International Journal of Remote Sensing, 26(22): 4885-4905. https://doi.org/10.1080/01431160500300222
  23. Lee, S. and R.G. Lathrop, 2006. Sub-pixel analysis of Landsat ETM+ using self-organizing map (SOM) neural networks for urban land-cover characterization, Ieee Transactions on Geoscience and Remote Sensing, 44(6): 1642-1654. https://doi.org/10.1109/TGRS.2006.869984
  24. Lu, D.S., Q.H. Weng, and G. Li, 2006. Residential population estimation using a remote sensing derived impervious surface approach, International Journal of Remote Sensing, 27(16): 3553-3570. https://doi.org/10.1080/01431160600617202
  25. Lu, D. and Q. Weng, 2006. Use of impervious surface in urban land use classification, Remote Sensing of Environment, 102 (1-2): 146-160. https://doi.org/10.1016/j.rse.2006.02.010
  26. Lu, D. and Q. Weng, 2007. A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28(5): 823-870. https://doi.org/10.1080/01431160600746456
  27. Phinn, S., M. Stanford, P. Scarth, A.T. Murray, and P.T. Shyy, 2002. Monitoring the composition of urban environments based on the Vegetationimpervious surface-Soil (V-I-S) model by subpixel analysis techniques, International Journal of Remote Sensing, 23: 4131-4153. https://doi.org/10.1080/01431160110114998
  28. Powell, R.L., D.A. Roberts, P.E. Dennison, and L.L. Hess, 2007. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil, Remote Sensing of Environment, 106: 253-267. https://doi.org/10.1016/j.rse.2006.09.005
  29. Ridd, M.K., 1995. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing:Comparative anatomy for cities, International Journal of Remote Sensing, 16 (12): 2165-2185. https://doi.org/10.1080/01431169508954549
  30. Roberts, D.A., M. Gardner, R. Church, S. Ustin, and R.O. Green, 1998. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment, 65: 267-279. https://doi.org/10.1016/S0034-4257(98)00037-6
  31. Sawaya, K.E., L.G. Olmanson, N.J. Heinert, P.L. Brezonik, and M.E. Bauer, 2003. Extending satellite remote sensing to local scales: land and Water resource monitoring using highresolution imagery, Remote Sensing of Environment, 88(12): 144-156. https://doi.org/10.1016/j.rse.2003.04.006
  32. Shackelford, A.K. and C.H. Davis, 2003. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas, Ieee Transactions on Geoscience and Remote Sensing, 41(10): 2354-2363. https://doi.org/10.1109/TGRS.2003.815972
  33. Stehman, S.V., 1996. Estimating the Kappa coefficient and its variance under stratified random sampling, Photogrammetry and Remote Sensing, 62(4): 401-407.
  34. Weng, Q., 2010. Remote sensing and GIS integration:Theories, Methods, and Applications, New York: McGrewHill.
  35. Weng, Q., 2012. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends, Remote Sensing of Environment, 117: 34-49. https://doi.org/10.1016/j.rse.2011.02.030
  36. Wenze, Y., S. Jianhua, W. Jiawei and X. Lihua, 2006. Remote sensing of spatial patterns of urban renewal using linear spectral mixture analysis:a case of central urban area of Shanghai (1997-2000), Chinese Science Bulleting, 51(8): 977-986. https://doi.org/10.1007/s11434-006-0977-8
  37. Wu, C.S. and A.T. Murray, 2003. Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment, 84: 493-505. https://doi.org/10.1016/S0034-4257(02)00136-0
  38. Wu, C., 2004. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery, Remote Sensing of Environment, 93: 480-492. https://doi.org/10.1016/j.rse.2004.08.003
  39. Wu, C.S. and A.T. Murray, 2007. Population estimation using Landsat Enhanced Thematic Mapper imagery, Geographical Analysis, 39(1): 26-43. https://doi.org/10.1111/j.1538-4632.2006.00694.x
  40. Xian, G. and M. Crane, 2005. Assessments of urban growth in the Tampa Bay watershed using remote sensing data, Remote Sensing of Environment, 97: 203-215. https://doi.org/10.1016/j.rse.2005.04.017
  41. Xian, G., 2008. Mapping impervious surface using classification and regression tree algorithm, In: Remote sensing of impervious surfaces, Weng, Q. (Editor) Taylor, Boca Raton Florida, pp.39-58.
  42. Yuan, F. and M.E. Bauer, 2006. Mapping impervious surface area using high resolution imagery: A Comparison of object-based and per pixel classification, Proc. of American Society for Photogrammetry & Remote Sensing 2006 Annual Conference Reno and Nevada, May. 1-5, (Reno, Nevada), pp. 1-8.
  43. Zhan, Q., M. Molenaar and K. Tempfli, 2002. Hierarchical image object-based structural analysis toward urban land use classification using high-resolution imagery and airborne LIDAR data, Proc. of the Remote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop, pp. 251-258.
  44. Zhou, W., A.R. Troy, and J.M. Grove, 2006. Measuring urban parcel lawn greenness by using an objectoriented classification approach, Geoscience and Remote Sensing Symposium in IEEE International Conference, Jul. 31- Aug. 4, Denver, CO, pp. 2693-2696.
  45. Zhou, W., A.R. Troy, and J.M. Grove, 2008. Objectbased land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data, Sensors, 8: 1613-1636.