Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
International Journal of Remote Sensing
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Topology-Based Hierarchical Clustering of Self-Organizing Maps
IEEE Transactions on Neural Networks
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Remotely sensed imagery is currently used as an efficient tool for agricultural management and monitoring. In addition, the use of remotely sensed imagery in Europe has been extended towards determination of the areas potentially eligible for the farmer subsidies under the Common Agricultural Policy (CAP), through interactive or automatic land cover identification. For accurate quantification and fast identification of agricultural land cover areas from the imagery, a hybrid method, which combines automated clustering of self-organizing maps with object based image analysis, and called SOM+OBIA, is proposed. Performance analysis on three test zones (using multi-temporal Rapideye imagery) indicates that for the basic land cover categories (forest, water, vegetated areas, bare areas and sealed surfaces), unsupervised classification with the proposed SOM+OBIA method achieves an identification accuracy comparable to the accuracy of the traditional interactive object oriented analysis, with considerably less user interaction.