Radial basis function neural networks classification using very high spatial resolution satellite imagery: An application to the habitat area of Lake Kerkini (Greece)

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dc.contributor.author Keramitsoglou, I en
dc.contributor.author Sarimveis, H en
dc.contributor.author Kiranoudis, CT en
dc.contributor.author Sifakis, N en
dc.date.accessioned 2014-03-01T01:23:00Z
dc.date.available 2014-03-01T01:23:00Z
dc.date.issued 2005 en
dc.identifier.issn 0143-1161 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/16760
dc.subject.classification Remote Sensing en
dc.subject.classification Imaging Science & Photographic Technology en
dc.subject.other Algorithms en
dc.subject.other Computational complexity en
dc.subject.other Ecology en
dc.subject.other Fuzzy sets en
dc.subject.other Imaging techniques en
dc.subject.other Maximum likelihood estimation en
dc.subject.other Pattern recognition en
dc.subject.other Satellites en
dc.subject.other Spectrum analysis en
dc.subject.other Fuzzy partition en
dc.subject.other Neural network complexities en
dc.subject.other Satellite images en
dc.subject.other Texture analysis en
dc.subject.other Radial basis function networks en
dc.subject.other artificial neural network en
dc.subject.other lake ecosystem en
dc.subject.other remote sensing en
dc.subject.other satellite imagery en
dc.subject.other spatial resolution en
dc.subject.other Eastern Hemisphere en
dc.subject.other Eurasia en
dc.subject.other Europe en
dc.subject.other Greece en
dc.subject.other Southern Europe en
dc.subject.other World en
dc.title Radial basis function neural networks classification using very high spatial resolution satellite imagery: An application to the habitat area of Lake Kerkini (Greece) en
heal.type journalArticle en
heal.identifier.primary 10.1080/01431160512331326594 en
heal.identifier.secondary http://dx.doi.org/10.1080/01431160512331326594 en
heal.language English en
heal.publicationDate 2005 en
heal.abstract This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of multispectral very high spatial resolution satellite images into 13 classes of various scales. For the development of the RBF classifiers, the innovative fuzzy means training algorithm is utilized, which is based on a fuzzy partition of the input space. The method requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied to the area of Lake Kerkini, which is a wetland of great ecological value, located in northern Greece. Eleven experiments were carried out in total in order to investigate the performance of the classifier using different input parameters (spectral and textural) as well as different window sizes and neural network complexities. For comparison purposes the same satellite scene was classified using the maximum likelihood (MLH) classification with the same set of training samples. Overall, the neural network classifiers outperformed the MLH classification by 10-17%, reaching a maximum overall accuracy of 78%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. © 2005 Taylor & Francis Group Ltd. en
heal.publisher TAYLOR & FRANCIS LTD en
heal.journalName International Journal of Remote Sensing en
dc.identifier.doi 10.1080/01431160512331326594 en
dc.identifier.isi ISI:000229988900006 en
dc.identifier.volume 26 en
dc.identifier.issue 9 en
dc.identifier.spage 1861 en
dc.identifier.epage 1880 en

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