The performance of pixel window algorithms in the classification of habitats using VHSR imagery

Keramitsoglou, I; Sarimveis, H; Kiranoudis, CT; Kontoes, C; Sifakis, N; Fitoka, E

dc.contributor.author	Keramitsoglou, I	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Kiranoudis, CT	en
dc.contributor.author	Kontoes, C	en
dc.contributor.author	Sifakis, N	en
dc.contributor.author	Fitoka, E	en
dc.date.accessioned	2014-03-01T01:25:16Z
dc.date.available	2014-03-01T01:25:16Z
dc.date.issued	2006	en
dc.identifier.issn	0924-2716	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17626
dc.subject	EUNIS	en
dc.subject	habitat classification	en
dc.subject	kernel based re-classification	en
dc.subject	RBF neural networks	en
dc.subject	support vector machines	en
dc.subject.classification	Geography, Physical	en
dc.subject.classification	Geosciences, Multidisciplinary	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Ecosystems	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Image sensors	en
dc.subject.other	Neural networks	en
dc.subject.other	Vectors	en
dc.subject.other	EUNIS	en
dc.subject.other	Habitat classification	en
dc.subject.other	Kernel based re-classification	en
dc.subject.other	RBF neural networks	en
dc.subject.other	Support vector machines	en
dc.subject.other	Algorithms	en
dc.subject.other	algorithm	en
dc.subject.other	artificial neural network	en
dc.subject.other	classification	en
dc.subject.other	habitat structure	en
dc.subject.other	mapping	en
dc.subject.other	satellite imagery	en
dc.subject.other	spatial resolution	en
dc.subject.other	wetland	en
dc.subject.other	Central Macedonia	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Kerkini Lake	en
dc.subject.other	Macedonia [Greece]	en
dc.subject.other	Serrai	en
dc.subject.other	Southern Europe	en
dc.title	The performance of pixel window algorithms in the classification of habitats using VHSR imagery	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.isprsjprs.2006.01.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.isprsjprs.2006.01.002	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	This study investigates the potential of three advanced pixel window classification methods for habitat mapping, namely Kernel based spatial Re-Classification (KRC), Radial Basis Function (RBF) neural networks (NN) and Support Vector Machines (SVM). KRC classifier takes into account the spatial arrangement and frequency of spectral classes present within a predefined square kernel. On the other hand, RBF-NN and SVM classifiers use a set of spectral parameters (digital numbers of training pixels, mean values and standard deviations within a specified window kernel) as input information. The fuzzy means clustering algorithm is utilized for training the RBF networks. This method is based on a fuzzy partition of the input space and requires only a short amount of time to determine both the structure and the parameters of the RBF-NN classifier. The radial basis function is also adopted as the kernel function in the implementation of the SVM methodology. The test area of the present study is Lake Kerkini, a wetland ecosystem located in Macedonia (Northern Greece). The methods are applied to a very high spatial resolution multispectral satellite image acquired by IKONOS-2. The nomenclature used is EUNIS, a detailed hierarchical habitat classification scheme. Several classification experiments are carried out using the same training samples in order to study the behaviour of the three classifiers and perform meaningful comparisons. Overall, all three classifiers performed satisfactorily; however the SVM and RBF-NN classifiers consistently outperformed KRC, reaching overall accuracies of 72% and 69%, respectively. (C) 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	ISPRS Journal of Photogrammetry and Remote Sensing	en
dc.identifier.doi	10.1016/j.isprsjprs.2006.01.002	en
dc.identifier.isi	ISI:000238475900001	en
dc.identifier.volume	60	en
dc.identifier.issue	4	en
dc.identifier.spage	225	en
dc.identifier.epage	238	en