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Annual Report 2009/2010 - JUWEL - Forschungszentrum Jülich

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Fig. 124: Segmentation using local mutual best fitting test and global segment removal of two<br />

scenes with simulated changes using parameters T = 75, T check = 75, T checktolerance = 0.5,<br />

w s = 0.1 and w comp = 0.5.<br />

Fig. 125: Segmentation using local best fitting test and local segment removal of two scenes<br />

with simulated changes using parameters T = 75, T check = 75, T checktolerance = 0.5, w s = 0.1<br />

and w comp = 0.5.<br />

Conclusion<br />

We presented some new ideas for addressing the issue of segmenting remote sensing<br />

imagery for object-based change detection. An enhanced procedure based on<br />

multiresolution segmentation was presented, and it was shown that satisfying results for<br />

simulated data can be achieved.<br />

Further developments are needed such as new consistency tests and segment removal<br />

strategies. Moreover, methods for enabling the user to easily select the segmentation<br />

parameters, e.g. by using training samples, would be helpful. Finally, the adapted<br />

multiresolution segmentation needs to be implemented as eCognition plugin for allowing its<br />

direct use in the proposed change detection workflow.<br />

References<br />

[1] A. Singh, Review Article Digital change detection techniques using remotely-sensed data, International<br />

Journal of Remote Sensing 10, 6 (1989) 989-1003.<br />

[2] R. Radke, S. Andra, O. Al-Kofahi, Image change detection algorithms: a systematic survey, IEEE<br />

Transactions on Image Processing 14, 3 (2005) 294-307.<br />

[3] M. J. Canty, Image Analysis, Classification, And Change Detection In Remote Sensing: With Algorithms<br />

For ENVI/IDL, Taylor & Francis Ltd, 2nd ed. (<strong>2009</strong>).<br />

[4] T. Blaschke, S. Lang, and G. Hay (Eds.), Object-Based Image Analysis Spatial Concepts for Knowledge-<br />

Driven Remote Sensing Applications, Series: Lecture Notes in Geoinformation and Cartography,<br />

Springer, Berlin (2008).<br />

[5] I. Niemeyer, P. R. Marpu, and S. Nussbaum, Change detection using object features, Blaschke, T.,<br />

Lang, S. & Hay, G. (Eds.), Object-Based Image Analysis Spatial Concepts for Knowledge-Driven<br />

Remote Sensing Applications, Series: Lecture Notes in Geoinformation and Cartography, Springer,<br />

Berlin (2008) 185-201.<br />

169

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