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Detailed, accurate and up-to-date land cover information is critical for environmental and socio-economic research (Heinl et al., 2009; Lu & Weng, 2007). A large number of satellite platforms are operational that have the capability to provide remotely sensed imagery at various spatial and temporal scales (Foody, 2002). This abundance of available data offers great potential for generating frequently updated thematic maps as remotely sensed images cover large areas, are acquired at regular intervals and are less costly than traditional ground-survey methods (Foody, 2009; Gao, 2009; Pal & Mather, 2004; Szuster et al., 2011). Current image-processing techniques are, however, limited in their ability to extract accurate land cover features automatically (Baraldi et al., 2010). Many factors also affect the accuracy of image classification (Lu & Weng, 2007) and the quality of many land cover maps is often perceived as being insufficient for operational use (Foody, 2002).
Supervised classification, an approach commonly used for the classification of remote sensing images, requires samples of known identity (training samples) to construct a model capable of classifying unknown samples. Apart from selecting a suitable classifier, the number and quality of training samples are key to a successful classification (Hubert-Moy et al., 2001; Lillesand et al., 2008; Lu & Weng, 2007). A sufficient number of training samples is generally required to perform a successful classification and the samples need to be well distributed and sufficiently representative of the land cover classes being evaluated (Campbell, 2006; Gao, 2009; Lu & Weng, 2007; Mather, 2004). In remote sensing applications, the availability of labelled training samples is often limited (Gehler & Schölkopf, 2009; Mountrakis et al., 2011) as their collection is time-consuming, expensive and tedious, often requiring the study of maps and aerial photographs and carrying out field visits (Campbell, 2006).
Support vector machines (SVM) have been shown to improve the reliability and accuracy of supervised classifications (Oommen et al., 2008). SVM are known for their good generalizing ability even when few training samples are available and it has been suggested that SVM produce superior results compared to other statistical classifiers when fewer training samples are available (Foody & Mathur, 2004b; Li et al., 2010; Lizarazo, 2008; Mountrakis et al., 2011; Pal & Mather, 2005).
The introduction of SVM to remote sensing has led to a number of comparative studies involving SVM and other classifiers of land cover (Camps-Valls et al., 2004; Camps-Valls & Bruzzone, 2005; Dixon & Candade, 2008; Foody & Mathur 2004a; Gualtieri & Cromp, 1998; Huang et al., 2002; Kavzoglu & Colkesen, 2009; Keuchel et al., 2003; Melgani & Bruzzone, 2002; 2004; Mercier & Lennon, 2003; Oommen et al., 2008; Pal & Mather 2004; 2005; Szuster et al., 2011; Tzotsos & Argialas, 2008). Although the results of such studies depend on the data and classification scheme used in each case, it was generally found that SVM produced either superior or equivalent classification accuracies when compared with methods such as maximum likelihood (ML), nearest neighbour (NN), artificial neural networks (ANN) and decision trees.