FUSION OF OPTICAL AND THERMAL IMAGERY AND LIDAR DATA FOR APPLICATION TO 3-D URBAN ENVIRONMENT - Pattern Recognition Applications in Remotely Sensed Hyperspectral Image Analysis

Anna Brook, Marijke Vandewal, Rudolf Richter, Eyal Ben-Dor

2012

Abstract

Investigation of urban environment includes a wide range of applications that require 3-D information. New approaches are needed for near-real-time analysis of the urban environment with natural 3-D visualization of extensive coverage. The remote sensing technology is a promising and powerful tool to assess quantitative information of urban materials and structures. This technique provides ability for easy, rapid and accurate in situ assessment of corrosion, deformations and ageing processes in the spatial (2-D) and the spectral domain within near-real-time and with high temporal resolution. LiDAR technology offers precise information about the geometrical properties of the surfaces and can reflect the different shapes and formations in the complex urban environment. Generating a monitoring system that is based on integrative fusion of hyperspectral, thermal and LiDAR data may enlarge the application envelope of each individual technology and contribute valuable information on the built urban environment. A fusion process defined by a data-registration algorithm and including spectral/thermal/spatial and 3-D information has been developed. The proposed practical 3-D urban environment application may provide urban planners, civil engineers and decision-makers with tools to consider temporal, quantitative and thermal spectral information in the 3-D urban space.

References

  1. Ameri, B., 2000. Automatic recognition and 3-D reconstruction of buildings from digital imagery. Thesis (PhD), University of Stuttgart.
  2. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing, 58, 239- 258
  3. Bouman, C. and Sauer K., 1993. A generalized Gaussian image model for edge-preserving map estimation. IEEE Trans. Image Processing, 2(3), 296-310.
  4. Brown, H., and Lowe, D., 2002. Invariant features from interest point groups, in BMVC.
  5. Brook, A. and Ben-Dor, E., 2011a. Advantages of boresight effect in the hyperspectral data analysis. Remote Sensing, 3 (3), 484-502.
  6. Brook, A. and Ben-Dor, E., 2011b. Supervised vicarious calibration of hyperspectral remote sensing data. Remote Sensing of Environment, 115, 1543-1555.
  7. Brook, A. and Ben-Dor, E., 2011c. Automatic registration of airborne and space-borne images by topology mapmatching with SURF. Remote Sensing, 3, 65-82.
  8. Brook, A., Ben-Dor, E., Richter, R., 2011. Modeling and monitoring urban built environment via multi-source integrated and fused remote sensing data. International Journal of Image and Data Fusion, in press, 1-31.
  9. Carlson, T. N., Dodd, J. K., Benjamin, S. G., and Cooper, J. N., 1981. Satellite estimation of the surface energy balance, moisture availability and thermal inertia. Journal of Applied Meteorology, 20, 67-87.
  10. Chantous, M., Ghosh, S., and Bayoumi, M. A., 2009. Multi-modal automatic image registration technique based on complex wavelets. In: Proceedings of the 16th IEEE International Conference on Image Processing, Cairo, Egypt, 173-176.
  11. Chen, S., and Billings, S. A., 1989. Recursive prediction error estimator for nonlinear models. International Journal of Control, 49, 569-594.
  12. Chou, P., and Brown, C., 1990. The theory and practice of Bayesian image labeling, Internat. J. Comput., 4, 185- 210.
  13. Cloude, S. P., Kootsookos, P. J., and Rottensteiner, F., 2004. The automatic extraction of roads from LIDAR data. In: ISPRS 2004, Istanbul, Turkey.
  14. Conel, J. E., 1969. Infrared Emissivities of Silicates: Experimental Results and a Cloudy Atmosphere Model of Spectral Emission from Condensed Particulate Mediums. Journal of Geophysical Research, 74 (6), 1614-1634.
  15. Donnay, J. P., Barnsley, M. J., and Longley, P. A., 2001. Remote sensing and urban analysis. In: J. P. Donnay, M. J. Barnsley and P. A. Longley, eds. Remote sensing and urban analysis. London and New York: Taylor and Francis, 3-18.
  16. Duchesne, P., and Bernatchez, L., 2002. AFLPOP: a computer program for simulated and real population allocation, based on AFLP data, Mol Ecol Notes, 2, 380-383.
  17. Herold, M., Goldstein, N. C., and Clarke, K. C., 2003. The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, 286-302.
  18. Herold, M., Couclelis, H., and Clarke, K. C., 2005. The role of spatial metrics in the analysis and modeling of land use change. Computers, Environment and Urban Systems, 29(4), 369-399.
  19. Hunt, G. R. and Vincent, R. K., 1968. The Behaviour of Spectral Features in the Infrared Emission from Particulate Surfaces of Various Grain Sizes. Journal of Geophysical Research, 73(18), 6039-6046.
  20. Jarvis, R. A., 1973. On the identification of the convex hull of a finite set of points in the plane. Information Processing Letters, 2, 18-21.
  21. Jarvis, R. A., 1977. Computing the shape hull of points in the plane. In: Proceedings of the IEEE Computer Society Conference Pattern Recognition and Image Processing, 231-241.
  22. Jensen, J. R. and Cowen, D. C., 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogrammetric Engineering and Remote Sensing, 65, 611-622.
  23. Juan, G., Martinez, M. and Velasco, R., 2007. Hyperspectral remote sensing application for semiurban areas monitorring. Urban Remote Sensing Joint Event, 11 (13), 1-5.
  24. Kidder, S. Q. and Wu, H-T., 1987. A multispectral study of the St. Louis area under snow-covered conditions using NOAA-7 AVHRR data. Remote Sensing of Environment, 22, 159-172.
  25. Kolbe, T. H., Gerhard, G. and Plümer, L., 2005. CityGML-Interoperable access to 3D city models. In: International Symposium on Geoinformation for Disaster Management GI4DM 2005, Delft, Netherlands, Lecture Notes in Computer Science, March, 2005.
  26. Lee, D. D., and Seung. H. S., 2001. Algorithms for nonnegative matrix factorization. In T. G. Dietterich and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13. Proceedings of the 2000 Conference: 556562, The MIT Press.
  27. Lin, C. J., 2007. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19, 2756-2779.
  28. Lindeberg, T., 2004. Feature detection with automatic scale selection. International Journal of Computer Vision, 30, 79-116.
  29. Masaharu, H. and Ohtsubo, K., 2002. A filtering method of airborne laser scanner data for complex terrain. The International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, 15 (3B), 165-169.
  30. Nichol, J. E., 1994. A GIS-based approach to microclimate monitoring in Singapore's high-rise housing estates. Photogrammetric Engineering and Remote Sensing, 60, 1225-1232.
  31. Nichol, J. E., 1996. High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellite-based study. Journal of Applied Meteorology, 35, 135-146.
  32. Pudil P., Novovicova J., Kittler J., 1994. Floating search methods in feature selection. Pattern Recognition Letters, 15, 1119-1125.
  33. Robila, S. A., and Maciak, L. G., 2006. A parallel unmixing algorithm for hyperspectral images. Technical report, Center for Imaging and Optics, Montclair State University.
  34. Roessner, S., Segl, K., Heiden, U. and Kaufmann, H., 2001. Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39 (7), 1525-1532.
  35. Roth, M., Oke, T. R., and Emery, W. J., 1989. Satellitederived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10, 1699-1720.
  36. Rottensteiner, F., Trinder, J., Clode, S., Kubic, K., 2003. Building detection using LIDAR data and multispectral images. In: Proceedings of DICTA, Sydney, Australia, 673-682.
  37. Rubila S. A., and Maciak L. G., 2009. Considerations on Parallelizing Nonnegative Matrix Factorization for Hyperspectral Data Unmiging. Geoscience and Remote Sensing Letters, IEEE, 6(1), 57-61.
  38. Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P., 1989. Design and analysis of computer experiments. Statistical Science, 4(4), 409-435.
  39. Salisbury, J. W., Hapke B., and Eastes, J. W., 1987. Usefulness of Weak Bands in Midinfrared Remote Sensing of Particulate Planetary Surfaces. Journal of Geophysical Research, 92, pp. 702-710.
  40. Segl, K., Roessner, S., Heiden, U., Kaufman. 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.
  41. Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C., 2001. Estimating the support of a high-dimensional distribution. Technical report, Microsoft Research, MSR-TR, 87-99.
  42. Steele, J. M., 2002. Minimum spanning trees for graphs with random edge lengths, Mathematics and Computer Science, 2, 223-245.
  43. Tao, V., 2001. Database-guided automatic inspection of vertically structured transportation objects from mobile mapping image sequences. In: ISPRS Press, 1401- 1409.UserGuide eCognition, 2003. Website: www. Definiens_imaging.com.
  44. Vapnik, V., 1998. Statistical learning theory. New York: Wiley.
  45. Velaga, N. R., Quddus, M. A. and Bristow, A. L., 2009. Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems. Transportation Research Part C: Emerging Technologies, 17, 672-683.
  46. Villa, A., Chanussot, J., Benediktsson, J. A., and Jutten, C., 2011. Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution. IEEE Selected Topics in Signal Processing, 5(3), 521 - 533.
  47. Vukovich, F. M., 1983. An analysis of the ground temperature and reflectivity pattern about St. Louis, Missouri, using HCMM satellite data. Journal of Climate and Applied Meteorology, 22, 560-571.
  48. Wang, J., and Shan, J., 2009. Segmentation of LiDAR point clouds for building extraction. In: ASPRS 2009 Annual Conference, Baltimore, MD.
  49. Whitney A. W., 1971. A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 20, 1100-1103.
  50. Yang, F., and Jiang, T., 2003. Pixon-based image segmentation with Markov random fields, IEEE Trans Image Process. 12(12):1552-9.
  51. Young, S. J., Johnson, R. B., and Hackwell, J. A., 2002. An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research, 107, 20-28.
  52. Zhou, G., 2004. Urban 3D GIS from LiDAR and digital aerial images. Computers and Geosciences, 30, 345- 353.
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Paper Citation


in Harvard Style

Brook A., Vandewal M., Richter R. and Ben-Dor E. (2012). FUSION OF OPTICAL AND THERMAL IMAGERY AND LIDAR DATA FOR APPLICATION TO 3-D URBAN ENVIRONMENT - Pattern Recognition Applications in Remotely Sensed Hyperspectral Image Analysis . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012) ISBN 978-989-8425-98-0, pages 394-405. DOI: 10.5220/0003845003940405


in Bibtex Style

@conference{prarshia12,
author={Anna Brook and Marijke Vandewal and Rudolf Richter and Eyal Ben-Dor},
title={FUSION OF OPTICAL AND THERMAL IMAGERY AND LIDAR DATA FOR APPLICATION TO 3-D URBAN ENVIRONMENT - Pattern Recognition Applications in Remotely Sensed Hyperspectral Image Analysis},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)},
year={2012},
pages={394-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003845003940405},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)
TI - FUSION OF OPTICAL AND THERMAL IMAGERY AND LIDAR DATA FOR APPLICATION TO 3-D URBAN ENVIRONMENT - Pattern Recognition Applications in Remotely Sensed Hyperspectral Image Analysis
SN - 978-989-8425-98-0
AU - Brook A.
AU - Vandewal M.
AU - Richter R.
AU - Ben-Dor E.
PY - 2012
SP - 394
EP - 405
DO - 10.5220/0003845003940405