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Erschienen in:
1997 | OriginalPaper | Buchkapitel
Generalisation of Neural Network Based Segmentation Results for Classification Purposes
verfasst von : Ari Visa, Markus Peura
Erschienen in: Neurocomputation in Remote Sensing Data Analysis
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper the automatic post-processing of segmented images is discussed. The segmentation based on local features and neural networks produces often small regions that disturb further analysis. Strategies for the elimination of these small regions are discussed. One approach based on pyramidal hierarchy is implemented. The approach is tested on a land-based cloud classification problem and the results are reported. This simple strategy applied on the cloud classification problem improves the result 20–30 per cent depending on the image. In future continuation of this work it is planned to study how the dynamical expanding context and learning grammars can improve the generalisation of the segmentation and the classification result.