By Bernadete Ribeiro, Rudolf F. Albrecht, Andrej Dobnikar, David W. Pearson, Nigel C. Steele
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Extra info for Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, .0002
The problem for an automatic approach here is the supervised part of the classification algorithm. Normally this part involves the work of a human operator and requires a lot of experience because the quality of the training areas is a crucial factor for the quality of the classification result. ), a method is needed to derive the training areas in an automatic way. Having assumed that the number of wrongly captured GIS objects and the number of changes in the real world are substantially less than the number of all GIS objects of the data set, the training areas can be derived automatically from the already existing GIS data.
A more detailed description of the verification of the classification results can be found in  . 3 Implementation The approach is implemented in a software package based on UNIX and X-Windows. Figure 2 shows a selection of different windows of the program. The software was implemented in such a way that all parameters of the approach can be changed interactively by the user and stored as a project. Additionally a visualization component is available to explore the results interactively on the screen.
1, No. 1. : Regularization Algorithms for Learning that are Equivalent to Multilayer Networks. Science, Vol. : Automatic Verification of GIS Data Using High Resolution Multispectral Data. In: International Archives of Photogrammetry and Remote Sensing (ISPRS). Vol. 32, Part 3/1. : Rule-based Interpretation of Aerial Images. IEEE Transactions on Pattern and Machine Intelligence. Vol. PAMI-7, No. 5. : From Ziplock Snakes to Velcro Surfaces. , Agouris, P. ): Automated Extraction of ManMade Objects from Aerial and Space Images.
Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, .0002 by Bernadete Ribeiro, Rudolf F. Albrecht, Andrej Dobnikar, David W. Pearson, Nigel C. Steele