Robust granular neural networks, fuzzy granules and classification
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
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Granulation of information is a new way to describe the increased complexity of natural phenomena. The lack of clear borders in nature calls for a more efficient way to process such data. Land use both in general but also as perceived in satellite images is a typical example of data that are inherently not clearly delimited. A granular neural network (GNN) approach is used here to facilitate land use classification. The GNN model used combines membership functions of spectral as well as non-spectral spatial information to produce land use categories. Spectral information refers to IRS satellite image bands and non-spectral data are here of topographic nature, namely slope, aspect and elevation. The processing is done through a standard neural network trained by back-propagation learning algorithm. A thorough presentation of the results is given in order to evaluate the merits of this method.