Object features for pixel-based classification of urban areas comparing different machine learning algorithms

Photogrammetrie, Fernerkundung, Geoinformation

Volumn 2013, Issue 3, 2013, Pages 149-161

  Wolf, Nils ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

Machine learning; Object based feature extraction; Spatialspectral classification; Urban remote sensing

Indexed keywords

EID: 84880646739     PISSN: 14328364     EISSN: None     Source Type: Journal    
DOI: 10.1127/1432-8364/2013/0166     Document Type: Article

References (35)

1. BAATZ, M. & SCHÄPE, A., 1999: Object-Oriented and Multi-Scale Image Analysis in Semantic Networks. -2nd International Symposium: Operationalization of Remote Sensing, 16-20 August, ITC, Niederlande.
2. BAATZ, M. & SCHÄPE, A., 2000: Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. -Angewandte Geographische Informationsverarbeitung XII: 12-23.
3. 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 58 (3-4): 239-258.
4. BLASCHKE, T., LANG, S. & HAY, G.J., 2008: Objectbased image analysis. -817 p., Springer, Berlin.
5. BLASCHKE, T., 2010: Object based image analysis for remote sensing. -ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2-16.
6. BREIMAN, L., FRIEDMAN, J.H., OLSHEN, R.A. & STONE, C.J., 1984: Classification and regression trees. -Chapman & Hall, New York, USA.
7. BREIMAN, L., 1996: Bagging predictors. -Machine Learning 24 (2): 123-140, New York, USA.
8. BREIMAN, L., 2001: Random Forests. -Machine Learning 45 (1): 5-32.
9. BURGES, C.J., 1998: A Tutorial on Support Vector Machines for Pattern Recognition. -Data Mining and Knowledge Discovery 2 (2): 121-167.
10. BURNETT, C. & BLASCHKE, T., 2003: A multi-scale segmentation/object relationship modelling methodology for landscape analysis. -Ecological Modelling 168 (3): 233-249.
11. CASTILLA, G. & HAY, G.J., 2008: Image objects and geographic objects. -BLASCHKE, T., LANG, S. & HAY, G.J. (eds.): Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications: 91-110, Springer-Verlag, Berlin.
12. CONGALTON, R.G. & GREEN, K., 1999: Assessing the accuracy of remotely sensed data. -Lewis Publications, Boca Raton, USA.
13. DURO, D.C., FRANKLIN, S.E. & DUBÉ, M.G., 2012: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. -Remote Sensing of Environment 118: 259-272.
14. EFRON, B. & TIBSHIRANI, R., 1994: An introduction to the bootstrap. -436 p., Chapman & Hall, New York, USA.
15. HEROLD, M., GARDNER, M. & ROBERTS, D.A., 2003: Spectral resolution requirements for mapping urban areas. -IEEE Transactions on Geoscience and Remote Sensing 41 (9): 1907-1919.
16. HOF, A. & SCHMITT, T., 2011: Urban and tourist land use patterns and water consumption: Evidence from Mallorca, Balearic Islands. -Land Use Policy 28 (4): 792-804.
17. HO, T.K., 1998: The random subspace method for constructing decision forests. -IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8): 832-844.
18. HUANG, C., DAVIS, L.S. & TOWNSHEND, J.R.G., 2002: An assessment of support vector machines for land cover classification. -International Journal of Remote Sensing 23 (4): 725-749.
19. KARATZOGLOU, A., SMOLA, A., HORNIK, K. & ZEILEIS, A., 2004: kernlab -An S4 Package for Kernel Methods in R. -Journal of Statistical Software 11 (9): 1-20.
20. LU, D. & WENG, Q., 2007: A survey of image classification methods and techniques for improving classification performance. -International Journal of Remote Sensing 28 (5): 823-870.
21. NOVACK, T., ESCH, T., KUX, H.J.H. & STILLA, U., 2011: Machine Learning Comparison between WorldView-2 andQuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification. -Remote Sensing 3 (10): 2263-2282.
22. PALMASON, J., BENEDIKTSSON, J.A., SVEINSSON, J. & CHANUSSOT, J., 2005: Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis. -IEEE International Geoscience and Remote Sensing Symposium: 176-179.
23. R CORE TEAM, 2012: R: A Language and Environment for Statistical Computing. -http://www.Rproject.org (4.11.2012).
24. SCHÖLKOPF, B. & SMOLA, A.J., 2002: Learning with kernels. -MIT Press, Cambridge, Massachusetts, USA.
25. STEHMAN, S.V. & CZAPLEWSKI, R.L., 1998: Design and Analysis for Thematic Map Accuracy Assessment. -Remote Sensing of Environment 64 (3): 331-344.
26. SVETNIK, V., LIAW, A., TONG, C., CULBERSON, J., SHERIDAN, R. & FEUSTON, B., 2003: Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. -Journal of Chemical Information and Modeling 43 (6): 1947-1958.
27. TARABALKA, Y., BENEDIKTSSON, J.A. & CHANUSSOT, J., 2009: Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques. -IEEE Transactions on Geoscience and Remote Sensing 47 (8): 2973-2987.
28. TARABALKA, Y., FAUVEL, M., CHANUSSOT, J. & BENEDIKTSSON, J.A., 2010: SVM-and MRF-Based Method for Accurate Classification of Hyper spectral Images. -IEEE Geoscience and Remote Sensing Letters 7 (4): 736-740.
29. THERNEAU, T.M. & ATKINSON, E.J., 1997: An introduction to recursive partitioning using the rpart routines. -http://mayoresearch.mayo.edu/mayo/ research/biostat/upload/61.pdf (13.11.2012).
30. TRIMBLE, 2012: eCognition® Developer 8.8. Reference Book. -Trimble Germany GmbH.
31. VAPNIK, V.N., 1998: Statistical learning theory. -Wiley, New York, USA.
32. VENABLES, W.N. & RIPLEY, B.D., 2002: Modern Applied Statistics with S. -4th ed., Springer, New York, USA.
33. WANG, L., SOUSA, W.P. & GONG, P., 2004: Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. -International Journal of Remote Sensing 25 (24): 5655-5668.
34. WASKE, B., BENEDIKTSSON, J.A., ÁRNASON, K. & SVEINSSON, J.R., 2009: Mapping of hyperspectral AVIRIS data using machine-learning algorithms. -Canadian Journal of Remote Sensing 35 (S1): 106-116.
35. WOLF, N., HOF, A. & JÜRGENS, C., 2012: Machine learning for the exhaustive evaluation of objectbased feature spaces. -GEOBIA 2012: 267-272.