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A New Tree-Based Classifier for Satellite Images
Published in IGI Global
Pages: 30 - 38

Classified maps play an important role in numerous remote sensing data applications, for example, land-cover change, forest degradation, hydrological modeling, wildlife habitat modeling, and biodiversity conservation. One of the most important products of a raw image is the classified map which labels the image pixels in meaningful classes. Several classifiers have been developed (e.g., Franklin, Peddle, Dechka & Stenhouse, 2002; Pal & Mather, 2003; Gallego, 2004) and implemented worldwide in software packages (e.g., ERDAS IMAGINE, ENVI, IDRISI and ArcGIS) for classifying satellite images. However, accurate prediction of pixel-wise class labels is still a challenge (Blinn, 2005; Song, Duan & Jiang, 2012). Among various classification methods, Maximum Likelihood (ML) classifier is the most widely used classifier because of its simplicity and availability in image processing softwares (Peddle, 1993). ML classifier is based on a parametric model that assumes normally distributed data, which is often violated in complex landscape satellite images (Lu & Weng, 2007). Non-parametric classifiers do not require stringent model assumptions like normality and gained much popularity. For instance, the classifiers based on k-nearest neighbor (k-NN), artificial neural network (ANN), decision trees and support vector machines (SVM) have shown better performance as compared to ML classifiers (Zhang & Wang, 2003; Bazi & Melgani, 2006; Li, Crawford & Jinwen, 2010; Atkinson & Naser, 2010). Comparison of the classifiers has been an active research area in machine learning. For example, Sudha & Bhavani (2012) concluded that SVM is better classifier than k-NN; and Song et al. (2012) demonstrated that SVM and ANN are comparable; however, SVM often performs slightly better than ANN.

About the journal
JournalEncyclopedia of Business Analytics and Optimization
PublisherIGI Global
Open AccessNo