Automated object-based classification of topography from SRTM data

Geomorphology

Volumn 141-142, Issue , 2012, Pages 21-33

  Drǎguţ, Lucian ^a,b   Eisank, Clemens ^a  

^a UNIVERSITY OF SALZBURG   (Austria)

^b WEST UNIVERSITY OF TIMISOARA   (Romania)

Author keywords

Complexity; Landform classification; Local variance; Object based image analysis (OBIA); Terrain segmentation; Web application

Indexed keywords

AUTOMATION; IMAGE ANALYSIS; LAND CLASSIFICATION; LAND SURFACE; LANDFORM EVOLUTION; REGIONALIZATION; SHUTTLE RADAR TOPOGRAPHY MISSION; TERRAIN; TOPOGRAPHY; WORLD WIDE WEB;

EID: 84856418204     PISSN: 0169555X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geomorph.2011.12.001     Document Type: Article

References (30)

1. Anselin L. Exploring Spatial Data with GeoDa: A Workbook 2005, Center for Spatially Integrated Social Science (CSISS), Urbana-Champaign, IL.
2. Baatz M., Schäpe A. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung 2000, 12-23. Wichmann-Verlag, Heidelberg. J. Strobl, T. Blaschke, G. Griesebner (Eds.).
3. Bittner T. Vagueness and the trade-off between the classification and delineation of geographic regions - an ontological analysis. International Journal of Geographical Information Science 2011, 25:825-850.
4. Blaschke T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 2010, 65:2-16.
5. Drǎgut L., Blaschke T. Automated classification of landform elements using object-based image analysis. Geomorphology 2006, 81:330-344.
6. Drǎgut L., Eisank C. Object representations at multiple scales from digital elevation models. Geomorphology 2011, 129:183-189.
7. Drǎgut L., Tiede D., Levick S. ESP: a tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science 2010, 24:859-871.
8. Drǎgut L., Eisank C., Strasser T. Local variance for multi-scale analysis in geomorphometry. Geomorphology 2011, 130:162-172.
9. Eisank C., Drǎgut L., Blaschle T. A generic procedure for semantics-oriented landform classification using object-based image analysis. Geomorphometry 2011 2011, 125-128. ESRI, Redlands, CA. T. Hengl, I.S. Evans, J.P. Wilson, M. Gould (Eds.).
10. Espindola G., Camara G., Reis I., Bins L., Monteiro A. Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing 2006, 27:3035-3040.
11. Evans I.S. What do terrain statistics really mean?. Landform Monitoring, Modelling and Analysis 1998, 119-138. Wiley, Chichester. S. Lane, K. Richards, J. Chandler (Eds.).
12. Evans I.S. Geomorphometry and landform mapping: what is a landform?. Geomorphology 2012, 137:94-106.
13. FAO FAO Interdisciplinary Database: Spatial Standards and Norms 2003.
14. Farr T.G., Rosen P.A., Caro E., Crippen R., Duren R., Hensley S., Kobrick M., Paller M., Rodriguez E., Roth L. The shuttle radar topography mission. Reviews of Geophysics 2007, 45:RG2004. 10.1029/2005RG000183.
15. Fenneman, N.M., Johnson, D.W., 1946. Physical division of the United States. U.S. Geological Survey, scale 1:7,000,000.
16. Gao Y., Mas J.F., Kerle N., Navarrete Pacheco J.A. Optimal region growing segmentation and its effect on classification accuracy. International Journal of Remote Sensing 2011, 32:3747-3763.
17. Hammond E.H. Small-scale continental landform maps. Annals of the Association of American Geographers 1954, 44:33-42.
18. Hammond E.H. Analysis of properties in land form geography: an application to broad-scale land form mapping. Annals of the Association of American Geographers 1964, 54:11-19.
19. Iwahashi J., Pike R.J. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology 2007, 86:409-440.
20. Jarvis A., Reuter H.I., Nelson A., Guevara E. Hole-filled seamless SRTM data V4 2008, International Centre for Tropical Agriculture (CIAT). http://srtm.csi.cgiar.org.
21. MacMillan R.A., Shary P.A. Chapter 9 landforms and landform elements in geomorphometry. Developments in Soil Science 2009, vol. 33:227-254. Elsevier, Amsterdam. T. Hengl, H.I. Reuter (Eds.).
22. Meybeck M., Green P., Vorosmarty C. A new typology for mountains and other relief classes: an application to global continental water resources and population distribution. Mountain Research and Development 2001, 21:34-45.
23. Pike R. The geometric signature: quantifying landslide-terrain types from digital elevation models. Mathematical Geology 1988, 20:491-511.
24. Pike R.J., Acevedo W., Card D.H. Topographic grain automated from digital elevation models. Proceedings, Auto-Carto 9, ASPRS/ACSM Baltimore MD, 2-7 April 1989 1989, 128-137.
25. Reuter H.I., Nelson A., Jarvis A. An evaluation of void-filling interpolation methods for SRTM data. International Journal of Geographical Information Science 2007, 21:983-1008.
26. Shortridge A., Messina J. Spatial structure and landscape associations of SRTM error. Remote Sensing of Environment 2011, 115:1576-1587.
27. van Asselen S., Seijmonsbergen A.C. Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology 2006, 78:309-320.
28. van Niekerk A. A comparison of land unit delineation techniques for land evaluation in the Western Cape, South Africa. Land Use Policy 2010, 27:937-945.
29. Wood W.F., Snell J.B. A Quantitative System for Classifying Landforms 1960, U.S. Army Quartemaster Research and Engineering Center, Natick, MA.
30. Woodcock C.E., Strahler A.H. The factor of scale in remote-sensing. Remote Sensing of Environment 1987, 21:311-332.