dc.creator | Vasilakos, A. | en |
dc.creator | Stathakis, D. | en |
dc.date.accessioned | 2015-11-23T10:53:21Z | |
dc.date.available | 2015-11-23T10:53:21Z | |
dc.date.issued | 2005 | |
dc.identifier | 10.1007/s00500-004-0412-5 | |
dc.identifier.issn | 1432-7643 | |
dc.identifier.uri | http://hdl.handle.net/11615/34354 | |
dc.description.abstract | 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. | en |
dc.source | Soft Computing | en |
dc.source.uri | <Go to ISI>://WOS:000229018800003 | |
dc.subject | geographical information systems | en |
dc.subject | granular neural networks | en |
dc.subject | computational intelligence | en |
dc.subject | fuzzy sets | en |
dc.subject | land use | en |
dc.subject | remote sensing | en |
dc.subject | IMAGERY | en |
dc.subject | MODELS | en |
dc.subject | COVER | en |
dc.subject | Computer Science, Artificial Intelligence | en |
dc.subject | Computer Science, | en |
dc.subject | Interdisciplinary Applications | en |
dc.title | Granular neural networks for land use classification | en |
dc.type | journalArticle | en |