{"refrec":{"BRefID":145336,"RR":"<b>Ibrahim, E.; Adam, S.; Monbaliu, J.</b> (2009). Assessment of unsupervised classification techniques for intertidal sediments, <b><i>in</i></b>: Maktav, D. (Ed.) <i>Remote sensing for a changing Europe: Proceedings of the 28th Symposium of Remote Sensing Laboratories, Istanbul, Turkey, 2–5 June 2008.</i> pp. 348-355","BEntID":138444,"PublicFlag":1,"CheckedFlag":0,"wosflag":null,"vabbflag":null,"RefStringPartII":", <b><i>in</i></b>: Maktav, D. (Ed.) <i>Remote sensing for a changing Europe: Proceedings of the 28th Symposium of Remote Sensing Laboratories, Istanbul, Turkey, 2–5 June 2008.</i> pp. 348-355","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Ibrahim, E.; Adam, S.; Monbaliu, J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Ibrahim, E. <i>et al.</i>","Englishabstract":"The aim of this study is to explore three techniques for unsupervised classification of airborne hyperspectral imagery of intertidal flats. The unsupervised classification techniques considered are k-means (hard clustering), the Gustafson-Kessel algorithm (Fuzzy clustering), and the mixture of Gaussians model (probabilistic clustering). The behavior and suitability of these techniques is analyzed for sediment classification. Artificial data sets based on real airborne and field spectra are used for this purpose. The sensitivity of the techniques is investigated on two spectral aspects: the effect of within class (intra-class) variability and the effect of spectral dimensionality using feature selection. This sensitivity is expressed as classification accuracy in terms of the Kappa statistic (?) that indicates how better the classification is than chance agreement. The results show that the three techniques are suitable for sediment classification. When there is no feature selection involved, the mixture of Gaussians results in the best classification results. 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