ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data

International Journal of Geographical Information Science

Volumn 24, Issue 6, 2010, Pages 859-871

  Drǎguţ, Lucian ^a,b   Tiede, Dirk ^a   Levick, Shaun R ^c  

^a UNIVERSITY OF SALZBURG   (Austria)

^b WEST UNIVERSITY OF TIMISOARA   (Romania)

^c GEOPHYSICAL LABORATORY   (United States)

Author keywords

Characteristic scales; Definiens; Local variance; OBIA; Tessellation

Indexed keywords

ESTIMATION METHOD; HETEROGENEITY; IMAGE ANALYSIS; IMAGE RESOLUTION; PARAMETERIZATION; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SEGMENTATION; SPATIAL RESOLUTION; THRESHOLD; VARIANCE ANALYSIS;

EID: 77951189897     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658810903174803     Document Type: Article

References (38)

1. Addink, E., de Jong, S. and Pebesma, E., 2007. The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 73, 905-912.
2. Aplin, P., 2006. On scales and dynamics in observing the environment. International Journal of Remote Sensing, 27, 2123-2140.
3. Baatz, M. and Schäpe, A., 2000. Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation. In: J. Strobl, T. Blaschke, and G. Griesebner, eds. Angewandte geographische informationsverarbeitung. Heidelberg: Wichmann-Verlag, 12-23.
4. Bauer, R. and Dahlquist, J., 1998. Technical market indicators: analysis and performance. New York: Wiley.
5. Benz, U.C., et al., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239-258.
6. Blaschke, T., 2003. Object-based contextual image classification built on image segmentation. Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop, 113-119.
7. Blaschke, T. and Strobl, J., 2001. What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. Zeitschrift für Geoinformationssysteme, 6, 12-17.
8. Blaschke, T., Lang, S., and Hay, G., 2008. Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Berlin: Springer-Verlag.
9. Burnett, C. and Blaschke, T., 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233-249.
10. Castilla, G., et al., 2009. The impact of thematic resolution on the patch-mosaic model of natural landscapes. Landscape Ecology, 24, 15-23.
11. Drǎguţ, L. and Blaschke, T., 2006. Automated classification of landform elements using object-based image analysis. Geomorphology, 81, 330-344.
12. Drǎguţ, L. and Blaschke, T., 2008. Terrain segmentation and classification using SRTM data. In: Q. Zhou, B. Lees, and G.-A. Tang, eds. Advances in digital terrain analysis. Berlin: Springer, 141-158.
13. Drǎguţ, L., et al., 2009. Optimization of scale and parametrization for terrain segmentation: an application to soil-landscape modeling. Computers and Geosciences, 35, 1875-1883.
14. Fisher, P., 1997. The pixel: a snare and a delusion. International Journal of Remote Sensing, 18, 679-685.
15. Goodchild, M.F., Yuan, M., and Cova, T.J., 2007. Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21, 239-260.
16. Hall, O., et al., 2004. Detecting dominant landscape objects through multiple scales: an integration of object-specific methods and watershed segmentation. Landscape Ecology, 19, 59-76.
17. Hay, G., Niemann, K., and Goodenough, D., 1997. Spatial thresholds, image-objects, and upscaling: a multiscale evaluation. Remote Sensing of Environment, 62, 1-19.
18. Hay, G., et al., 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observations and Geoinformation, 7, 339-359.
19. Kim, M., Madden, M., and Warner, T., 2008. Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. In: T. Blaschke, S. Lang, and G.J. Hay, eds. Object-based image analysis-spatial concepts for knowledge driven remote sensing applications. Berlin: Springer, 291-307.
20. Lamonaca, A., Corona, P., and Barbati, A., 2008. Exploring forest structural complexity by multi-scale segmentation of VHR imagery. Remote Sensing of Environment, 112, 2839-2849.
21. Lang, S. and Langanke, T., 2006. Object-based mapping and object-relationship modeling for land use classes and habitats. Photogrammetrie, Fernerkundung, Geoinformation, 1, 5-18.
22. Lees, B., 2006. The spatial analysis of spectral data. Extracting the neglected data. Applied GIS, 2, 14.1-14.13.
23. Levick, S.R. and Rogers, K.H., 2008. Structural biodiversity monitoring in savanna ecosystems: integrating LiDAR and high resolution imagery through object-based image analysis. In: T. Blaschke, S. Lang, and G.J. Hay, eds. Object-based image analysis-spatial concepts for knowledge driven remote sensing applications. Berlin: Springer, 477-491.
24. Lhermitte, S., et al., 2008. Hierarchical image segmentation based on similarity of NDVI time series. Remote Sensing of Environment, 112, 506-521.
25. Luscier, J.D., et al., 2006. Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Frontiers in Ecology and the Environment, 4, 408-413.
26. Marceau, D., 1999. The scale issue in social and natural sciences. Canadian Journal of Remote Sensing, 25, 347-356.
27. Marceau, D. and Hay, G., 1999. Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing, 25, 357-366.
28. Meinel, G. and Neubert, M., 2004. A comparison of segmentation programs for high resolution remote sensing data. Proceedings of 20th ISPRS Congress. Istanbul.
29. Möller, M., et al., 2008. Placing soil-genesis and transport processes into a landscape context: a multiscale terrain-analysis approach. Journal of Plant Nutrition and Soil Science, 171, 419-430.
30. Ryherd, S. and Woodcock, C., 1996, Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing, 62, 181-194.
31. Schiewe, J., Tufte, L., and Ehlers, M., 2001. Potential and problems of multi-scale segmentation methods in remote sensing. GIS - Zeitschrift fur Geoinformationssysteme, 6, 34-39.
32. Silván-Cárdenas, J.L., Wang, L., and Zhan, F.B., 2009. Representing geographical objects with scaleinduced indeterminate boundaries: a neural network-based data model. International Journal of Geographical Information Science, 23, 295-318.
33. Smith, G., et al., 2007. UK land cover map production through the generalisation of OS MasterMap (R). Cartographic Journal, 44, 276-283.
34. Strahler, A.H., Woodcock, C.E., and Smith, J.A., 1986. On the nature of models in remote sensing. Remote Sensing of Environment, 20, 121-139.
35. Tiede, D. and Hoffmann, C., 2006. Process oriented object-based algorithms for single tree detection using laser scanning. In: T. Koukal and W. Schneider, eds. International workshop '3D Remote Sensing in Forestry'. Vienna, 162-167.
36. Tiede, D. and Lang, S., 2008. Rapid dwelling extraction from VHSR satellite imagery in refugee camps and dissemination of conditioned information via virtual globes. In: M. Ehlers, et al., eds. Digital Earth Summit on Geoinformatics 2008: tools for global change research. Heidelberg:Wichmann, 100-105.
37. Van Der Werff, H.M.A. and Van Der Meer, F.D., 2008. Shape-based classification of spectrally identical objects. ISPRS Journal of Photogrammetry and Remote Sensing, 63, 251-258.
38. Woodcock, C. and Strahler, A., 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21, 311-332.