Terrain segmentation and classification using srtm data

Lecture Notes in Geoinformation and Cartography

Volumn 0, Issue , 2008, Pages 141-158

  Drăguţ, Lucian D ^a   Blaschke, Thomas ^a  

^a NONE

Author keywords

Accuracy assessment; Fuzzy logic; Geomorphometry; SRTM; Terrain segmentation

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER CIRCUITS; FUZZY LOGIC; IMAGE SEGMENTATION; OBJECT ORIENTED PROGRAMMING; TRACKING RADAR;

ACCURACY ASSESSMENT; CLASSIFICATION SYSTEM; CLASSIFIEDS; FUZZY-LOGIC; GEOMORPHOMETRY; HIGH MOUNTAINS; SHUTTLE RADAR TOPOGRAPHY MISSION; SLOPE GRADIENTS; TERRAIN CLASSIFICATION; TERRAIN SEGMENTATIONS;

LANDFORMS;

EID: 68849094692     PISSN: 18632246     EISSN: 18632351     Source Type: Book Series    
DOI: 10.1007/978-3-540-77800-4_8     Document Type: Conference Paper

References (40)

1. Baatz, M. and Schäpe, A., (2000), Multi-resolution segmentation. An optimization approach for high-quality multiscale image segmentation, Angewandte Geographische Informationsverarbeitung XII, Heidelberg: Wichmann Verlag: 12–23.
2. Beven, K., (2006), A manifesto for the equifinality thesis, Journal of Hydrology, 320: 18–36.
3. Bishop, M., Shroder, J. and Colby, J., (2003), Remote sensing and geomorphometry for studying relief production in high mountains, Geomorphology, 55: 345–361.
4. Blaschke, T. and Strobl, J., (2001), What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS, GIS – Zeitschrift für Geoinformationssysteme, 14: 12–17.
5. Blaschke, T. and Dragut, L., (2003), Integration of GIS and object-based image analysis to model and visualize landscapes. In ISPRS workshop “Challenges in Geospatial Analysis, Integration and Visualization II”, 8–9 September, 2003, Stuttgart, Germany, pp. 18-23.
6. Blaschke, T., Burnett, C. and Pekkarinen, A., (2004), New contextual approaches using image segmentation for object-based classification, In de Meer, F. and. de Jong, S.: Remote Sensing Image Analysis: Including the spatial domain, Dordrecht: Kluver Academic Publishers: 211–236.
7. Bolongaro-Crevenna, A., Torres-Rodriguez, V., Sorani, V., Frame, D. and Ortiz, M.A., (2005), Geomorphometric analysis for characterizing landforms in Morelos State, Mexico, Geomorphology, 67: 407–422.
8. Bue, B.D. and Stepinski, T.F., (2006), Automated classification of landforms on Mars, Computers & Geosciences, 32: 604- 614.
9. Burnett, C. and Blaschke, T., (2003), A multi-scale segmentation/object relationship modelling methodology for landscape analysis, Ecological Modelling, 168: 233–249.
10. Burrough, P.A., van Gaans, P.F. and MacMillan, R.A., (2000), High resolution landform classification using fuzzy k-means, Fuzzy Sets Syst., 113: 37–52.
11. CGIAR-CSI (2004), SRTM Data Processing Methodology, Available: on-line: http://srtm.csi.cgiar.org/SRTMdataProcessingMethodology.asp.
12. Congalton, R.G., (1991), A review of assessing the accuracy of classification of remotely sensed data, Remote. Sens. Environ., 37: 35–46.
13. Congalton, R.G. and Green, K., (1993), A practical look at the sources of confusion in error matrix generation, Photogrammetric Engineering & Remote Sensing, 59: 641–644.
14. Demoulin, A., Bovy, B., Rixhon, G. and Cornet, Y., (2007), An automated method to extract fluvial terraces from digital elevation models: The Vesdre valley, a case study in eastern Belgium, Geomorphology, 91: 51–64.
15. Dikau, R., (1989), The application of a digital relief model to landform analysis, In Raper, J.F. (ed.): Three dimensional applications in Geographical Information Systems, London: Taylor and Francis: 51–77.
16. Dragut, L. and Blaschke, T., (2006), Automated classification of landform elements using object-based image analysis, Geomorphology, 81: 330–344.
17. Evans, I.S., (1998), What do terrain statistics really mean? In Lane, S., Richards, K. and Chandler, J.: Landform Monitoring, Modelling and Analysis, Chichester: Wiley: 119–138.
18. Evans, I.S., (2006), Local aspect asymmetry of mountain glaciation: a global survey of consistency of favoured directions for glacier numbers and altitudes, Geomorphology, 73: 166–184.
19. Fisher, P., Wood, J. and Cheng, T., (2004), Where is Helvellyn? Fuzziness of multi-scale landscape morphometry, Trans Inst Br Geogr, 29: 106–128.
20. Fisher, P., Wood, J. and Cheng, T., (2005), Fuzziness and ambiguity in multi-scale analysis of landscape morphometry, In Petry, F.E., Robinson, V.B. and Cobb, M.A.: Fuzzy Modeling with Spatial Information for Geographic Problems, Berlin, Heidelberg, New York: Springer: 209–232.
21. Goudie, A.S., (2002), Aesthetics and relevance in geomorphological outreach, Geomorphology, 47: 245–249.
22. Haralick, R. and Shapiro, L., (1985), Survey: image segmentation techniques, Computer Vision, Graphics, and Image Processing, 29: 100–132.
23. Hay, G.J. and Castilla, G., (2006), Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT), International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences vol. XXXVI-4/C42, (CD ROM).
24. Irvin, B.J., Ventura, S.J. and Slater, B.K., (1997), Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin, Geoderma, 77: 137–154.
25. Iwahashi, J. and Pike, R.J., (2007), Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature, Geomorphology, 86: 409–440.
26. Lagacherie, P., Cazemier, D.R., van Gaans, P.F.M. and Burrough, P.A., (1997), Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area, Geoderma, 77: 197–216.
27. Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., (2004), Remote sensing and image interpretation, John Wiley & Sons.
28. Minár, J. and Evans, I.S., (2007), Elementary forms for land surface segmentation: the theoretical basis of terrain analysis and geomorphological mapping, Geomorphology, doi: 10.1016/j.geomorph.(2007.),06.003.
29. Pal, R. and Pal, K., (1993), A review on image segmentation techniques, Pattern Recognition, 26: 1277–1294.
30. Phillips, J.D., (2005), Weathering instability and landscape evolution, Geomorphology, 67: 255–272.
31. Pike, R.J., (1988), The geometric signature - Quantifying landslide - terrain types from digital elevation models, Mathematical Geology, 20: 491–511.
32. Pike, R.J., (1995), Geomorphometry - progress, practice, and prospect, Zeitschrift fur Geomorphologie Supplementband, 101: 221–238.
33. Pike, R.J., (2000), Geomorphometry: diversity in quantitative surface analysis, Prog. Phys. Geogr., 24: 1–20.
34. Rasemann, S., Schmidt, J., Schrott, L. and Dikau, R., (2004), Geomorphometry in mountain terrain, In Bishop, M. and Shroder, J.F.: GIS in mountain geomorphology, Berlin: Springer: 101–146.
35. Rossiter, D.J., (2001), Assessing the thematic accuracy of area-class soil maps. [On-line]. Available: http://www.itc.nl/~rossiter/docs/AccuracySoilMaps_Preprint31Aug2001,pdf
36. Schmidt, J. and Dikau, R., (1999), Extracting geomorphometric attributes and objects from digital elevation models- semantics, methods, future needs. In Dikau, R. and Saurer, H.: GIS for Earth Surface Systems: Analysis and Modelling of the Natural Environment, Berlin, Stuttgart: Borntraeger: 153–173.
37. Senin, N., Ziliotti, M. and Groppetti, R., (2007), Three-dimensional surface topography segmentation through clustering, Wear, 262: 395–410.
38. Smith, B. and Mark, D.M., (2003), Do mountain exist? Towards an ontology of landforms, Environment and Planning B. Planning and Design, (30)3: 411–427.
39. Tiede, D. and Hoffmann, C., (2006), Process oriented object-based algorithms for single tree detection using laser scanning data, EARSeL-Proceedings of the Workshop on 3D Remote Sensing in Forestry, 14th-15th Feb 2006, Vienna.
40. Wood, J., (1996), The Geomorphological Characterisation of Digital Elevation Models, Ph.D. thesis [On-line]. Available: http://www.soi.city.ac.uk/~jwo/phd/