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

Keramitsoglou, I; Sarimveis, H; Kiranoudis, CT; Sifakis, N

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