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Towards a vector agent modelling approach for remote sensing image classification

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REFERENCES

  • Baatz, M., Hoffmann, C., & Willhauck, G. (2007) Progressing from object-based to object-oriented image analysis. In: Blaschke, T., Land, S., & Hay, G.J., eds. Object-based Image Analysis: Spatial Concepts for Knowledge-driven Remote Sensing, Springer, Berlin, pp. 29–42.
  • Benenson, I., & Torrens, P. (2004) Geosimulation: Automata-based Modelling of Urban Phenomena, Wiley, England.
  • 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 and Remote Sensing, vol. 58, no. 3–4, pp. 239–258.
  • Chen, Y., Su, W., Li, J., & Sun, Z. (2009) Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, vol. 43, no. 7, pp. 1101–1110.
  • Cohn, A., & Gotts, N. (1996) The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Burrough, P., & Frank, A., eds. Proceedings GISDATA Specialist Meeting on Spatial Objects with Undetermined Boundaries, Taylor and Francis, London, pp. 171–187.
  • Fonseca, C.M., & Fleming, P.J. (1996) On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In:Voigt, H., Ebeling, W., Rechenberg, I. & Schwefel, H., eds. Proceedings Parallel Problem Solving from Nature PPSN IV Conference, Springer, Berlin. pp. 584–593.
  • Gao, J. (2009) Digital Analysis of Remotely Sensed Imagery, McGraw-Hill, New York.
  • Gitas, I., Mitri, G., & Ventura, G. (2004) Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sensing of Environment, vol. 92, no. 3, pp. 409–413.
  • Goodchild, M.F., & Cova, T.J. (2007) Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, vol. 21, no. 3, pp. 239–260.
  • Hammam, Y., Moore, A., & Whigham, P. (2007) The dynamic geometry of geographical vector agents. Computers, Environment and Urban Systems, vol. 31, no. 5, pp. 502–519.
  • Hay, G.J., Castilla, G., Wulder, M.A., & Ruiz, J.R. (2005) An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, vol. 7, no. 4, pp. 339–359.
  • Howe, T., Collier, N., North, M., Parker, M., & Vos, J. (2006) Containing agents: Contexts, projections and agents, Proceedings of the Agent 2006 Conference on Social Agents: Results and Prospects, Argonne National Laboratory, Argonne, Illinois.
  • Lu, D., & Weng, Q. (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, vol. 28, no. 5, pp. 823–870.
  • Moore, A. (2011) Geographical vector agent based simulation for agricultural land use modelling. In: Marceau, D., & Benenson, I., eds. Advanced GeoSimulation Models, Bentham, Beijing, pp. 30–48.
  • Moreno, N., Wang, F., & Marceau, D.J. (2009) Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model. Computers, Environment and Urban Systems, vol. 33, no. 1, pp. 44–54.
  • Moreno, N., Wang, F., & Marceau, D.J. (2010) A geographic object-based approach in cellular automata modeling. Photogrammetric Engineering and Remote Sensing, vol. 76, no. 2, pp. 183–191.
  • Okabe, A., Boots, B. & Sugihara, K. (1992) Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, Wiley, England.
  • Repast (2013) Recursive porous agent simulation toolkit. Available from: http://repast.sourceforge.net/index.html. [accessed 17 September 2013].
  • Schiewe, J., Tufte, L., & Ehlers, M. (2001) Potential and problems of multi-scale segmentation methods in remote sensing. GIS - Geo-Informationssysteme, vol. 6, pp. 34–39.
  • Sohn, G., & Dowman, I. (2007) Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS Journal of Photogrammetry & Remote Sensing, vol. 62, no. 1, pp. 43–63.
  • Thomas, N., Hendrix, C., & Congalton, R. (2003) A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing, vol. 69, no. 9, pp. 963–972.
  • Tian, J., & Chen, D.M. (2007) Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition. International Journal of Remote Sensing, vol. 28, no. 20, pp. 4625–4644.
  • Torrens, P., & Benenson, I. (2005) Geographic automata systems. International Journal of Geographical Information Science, vol. 19, no. 4, pp. 385–412.
  • Walter, V. (2004) Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry & Remote Sensing, vol. 58, no. 3–4, pp. 225–238.

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