there is also a need to improve the procedure of optimum scale selection. This may
include automated determination of optimum multiscales, development of more
sophisticated ways to assess image objects, and development of metrics to describe
object size regardless of the segmentation algorithms or data used.
Computational efficiency associated with selecting optimum scale should also be
explored. The improvements related to optimum scale selection will likely be a major
trend in OBIA within the remote sensing field for the next years.
REFERENCES
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 & Remote Sensing 73(8):905–912.
Aguirre-Gutiérrez, J., Seijmonsbergen, A., and Duivenvoorden, J. 2012. Optimizing
land cover classification accuracy for change detection, a combined pixel-based
and object-based approach in a mountainous area in Mexico. Applied Geography
34:29–37.
Aksoy, B., and Ercanoglu, M. 2012. Landslide identification and classification by object-based
image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey).
Computers & Geosciences 38:87–98.
Anders, N., Seijmonsbergen, A., and Bouten, W. 2011. Segmentation optimization and
stratified object-based analysis for semi-automated geomorphological mapping. Remote
Sensing of Environment 115:2976–2985.
Arnesen, A., Silva, T., Hess, L., Novo, E., Rudorff, C., Chapman, B., and McDonald, K. 2013.
Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR
ScanSAR images. Remote Sensing of Environment 130:51–61.
Baatz, M., Benz, U., Dehghani, S., and Heynen, M. 2004. eCognition User Guide 4. Munich:
Definiens Imagine.
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.) Heidelberg: Angewandte Geographische Informationsverarbeitung XII, 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 58:239–258.
Blaschke, T. 2005. A framework for change detection based on image objects. In S. Erasmi, B.
Cyffka, and M. Kappas (Eds.) Göttinger Geographische Abhandlungen, 113,Göttingen,
pp. 1–9.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65:2–16.
Bontemps, S., Bogaert, P., Titeux, N., and Defourny, P. 2008. An object-based change
detection method accounting for temporal dependences in time series with medium to
coarse spatial resolution. Remote Sensing of Environment 112:3181–3191.
Cai, Y., Tong, X., and Shu, R. 2009. Multi-scale segmentation of remote sensing image based
on watershed transformation. Urban Remote Sensing Joint Event, May 20–22.
REFERENCES
207
include automated determination of optimum multiscales, development of more
sophisticated ways to assess image objects, and development of metrics to describe
object size regardless of the segmentation algorithms or data used.
Computational efficiency associated with selecting optimum scale should also be
explored. The improvements related to optimum scale selection will likely be a major
trend in OBIA within the remote sensing field for the next years.
REFERENCES
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 & Remote Sensing 73(8):905–912.
Aguirre-Gutiérrez, J., Seijmonsbergen, A., and Duivenvoorden, J. 2012. Optimizing
land cover classification accuracy for change detection, a combined pixel-based
and object-based approach in a mountainous area in Mexico. Applied Geography
34:29–37.
Aksoy, B., and Ercanoglu, M. 2012. Landslide identification and classification by object-based
image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey).
Computers & Geosciences 38:87–98.
Anders, N., Seijmonsbergen, A., and Bouten, W. 2011. Segmentation optimization and
stratified object-based analysis for semi-automated geomorphological mapping. Remote
Sensing of Environment 115:2976–2985.
Arnesen, A., Silva, T., Hess, L., Novo, E., Rudorff, C., Chapman, B., and McDonald, K. 2013.
Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR
ScanSAR images. Remote Sensing of Environment 130:51–61.
Baatz, M., Benz, U., Dehghani, S., and Heynen, M. 2004. eCognition User Guide 4. Munich:
Definiens Imagine.
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.) Heidelberg: Angewandte Geographische Informationsverarbeitung XII, 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 58:239–258.
Blaschke, T. 2005. A framework for change detection based on image objects. In S. Erasmi, B.
Cyffka, and M. Kappas (Eds.) Göttinger Geographische Abhandlungen, 113,Göttingen,
pp. 1–9.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65:2–16.
Bontemps, S., Bogaert, P., Titeux, N., and Defourny, P. 2008. An object-based change
detection method accounting for temporal dependences in time series with medium to
coarse spatial resolution. Remote Sensing of Environment 112:3181–3191.
Cai, Y., Tong, X., and Shu, R. 2009. Multi-scale segmentation of remote sensing image based
on watershed transformation. Urban Remote Sensing Joint Event, May 20–22.
REFERENCES
207