An advantage of the developed OBIA framework is that it is composed of free (as
in freedom) software. For the implementation of this research a number of free and
open-source libraries were used (MSEG, cvAML, libSVM, OrfeoToolbox, and
GDAL). The coding was performed in the C++ and Python programming languages.
A disadvantage of the developed OBIA framework is that a knowledge-based
classification solution is not yet integrated but is currently being worked on.
Some of the topics for further research and development are extension of the
developed OBIA framework to integrate knowledge-based classification, solutions
for object-specific extraction tasks based on scale-space shape priors, and adaptation
of the developed methodology to specific remote sensing applications.
ACKNOWLEDGMENTS
The authors would like to thank the anonymous reviewers for their constructive
comments and suggestions.
REFERENCES
Akcay, H., and Aksoy, S. 2008. Automatic detection of geospatial objects using multiple
hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing
46:2097–2111.
Aplin, P., and Smith, G. 2008. Advances in object-based image classification. International
Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 37(Part B7):
725–728.
Baatz, M., and Schape, A. 2000. Multiresolution segmentation an optimization approach for
high quality multi-scale image segmentation. In J. Strobl, et al. (Eds.), Angewandte
Geographische Infor-mationsverarbeitung XII. Heidelberg: Wichmann, pp. 12–23.
Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for gis ready information.
ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4):239–258.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65(1):2–16.
Blaschke, T., Burnett, C., and Pekkarinen, A. 2004. Image segmentation methods for objectbased analysis and classification. In S. M.de Jong and F. D.van der Meer (Eds.), Remote
Sensing and Digital Image Analysis: Including the Spatial Domain. Dordrecht: Kluwer
Academic, pp. 211–236.
Blaschke, T., and Hay, G. 2001. Object-oriented image analysis and scale-space: Theory and
methods for modeling and evaluating multi–scale landscape structure. Interanational
Archive of Photogrammetry and Remote Sensing 34(Part4/W5): 22–29.
Blaschke, T., Lang, S., and Hay, G. 2008. Object Based Image Analysis—Spatial Concepts for
Knowledge Driven Remote Sensing Applications. New York: Springer.
Camps-Valls, G., and Bruzzone, L. 2005. Kernel-based methods for hyperspectral image
classification. IEEE Transactions on Geoscience and Remote Sensing 43(6):1351–1362.
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MULTISCALE SEGMENTATION AND CLASSIFICATION
in freedom) software. For the implementation of this research a number of free and
open-source libraries were used (MSEG, cvAML, libSVM, OrfeoToolbox, and
GDAL). The coding was performed in the C++ and Python programming languages.
A disadvantage of the developed OBIA framework is that a knowledge-based
classification solution is not yet integrated but is currently being worked on.
Some of the topics for further research and development are extension of the
developed OBIA framework to integrate knowledge-based classification, solutions
for object-specific extraction tasks based on scale-space shape priors, and adaptation
of the developed methodology to specific remote sensing applications.
ACKNOWLEDGMENTS
The authors would like to thank the anonymous reviewers for their constructive
comments and suggestions.
REFERENCES
Akcay, H., and Aksoy, S. 2008. Automatic detection of geospatial objects using multiple
hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing
46:2097–2111.
Aplin, P., and Smith, G. 2008. Advances in object-based image classification. International
Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 37(Part B7):
725–728.
Baatz, M., and Schape, A. 2000. Multiresolution segmentation an optimization approach for
high quality multi-scale image segmentation. In J. Strobl, et al. (Eds.), Angewandte
Geographische Infor-mationsverarbeitung XII. Heidelberg: Wichmann, pp. 12–23.
Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for gis ready information.
ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4):239–258.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65(1):2–16.
Blaschke, T., Burnett, C., and Pekkarinen, A. 2004. Image segmentation methods for objectbased analysis and classification. In S. M.de Jong and F. D.van der Meer (Eds.), Remote
Sensing and Digital Image Analysis: Including the Spatial Domain. Dordrecht: Kluwer
Academic, pp. 211–236.
Blaschke, T., and Hay, G. 2001. Object-oriented image analysis and scale-space: Theory and
methods for modeling and evaluating multi–scale landscape structure. Interanational
Archive of Photogrammetry and Remote Sensing 34(Part4/W5): 22–29.
Blaschke, T., Lang, S., and Hay, G. 2008. Object Based Image Analysis—Spatial Concepts for
Knowledge Driven Remote Sensing Applications. New York: Springer.
Camps-Valls, G., and Bruzzone, L. 2005. Kernel-based methods for hyperspectral image
classification. IEEE Transactions on Geoscience and Remote Sensing 43(6):1351–1362.
192
MULTISCALE SEGMENTATION AND CLASSIFICATION