Browsing by Author "Myburgh, Gerhard"
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- ItemEffect of feature dimensionality on object-based land cover classification : a comparison of three classifiers(CONSAS Conference, 2013) Myburgh, Gerhard; Van Niekerk, AdriaanThe efficient mapping of land cover from remotely sensed data is highly desirable as land cover information is essential for a range of environmental and socio-economic applications. Supervised classifiers are often applied in remote sensing to extract land cover information. While spectral information is typically used as the main discriminating features for such classifiers, additional features such as vegetation indices, transformed spectral data, textural information, contextual information and ancillary data may also considerably influence the accuracy of classification. Geographic object-based image analysis (GEOBIA) allows the easy integration of such additional features into the classification process. This paper compares the performance of three supervised classifiers in a GEOBIA environment as an increasing number of object features are included as classification input. Classification tree analysis (CTA) was employed for feature selection and importance ranking. Object features were considered in the order of their obtained rank. The support vector machine (SVM) produced superior classification accuracies when compared to those of nearest neighbour (NN) and maximum likelihood (ML) classifiers. Both SVM and NN produced stable results as the feature-set size was increased towards the maximum (22 features). ML’s performance, however, decreased considerably when few training samples are used and when the feature-set size (dimensionality) is increased.
- ItemThe impact of training set size and feature dimensionality on supervised object-based classification : a comparison of three classifiers(Stellenbosch : Stellenbosch University, 2012-12) Myburgh, Gerhard; Van Niekerk, Adriaan; Stellenbosch University. Faculty of Science. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Supervised classifiers are commonly used in remote sensing to extract land cover information. They are, however, limited in their ability to cost-effectively produce sufficiently accurate land cover maps. Various factors affect the accuracy of supervised classifiers. Notably, the number of available training samples is known to significantly influence classifier performance and to obtain a sufficient number of samples is not always practical. The support vector machine (SVM) does perform well with a limited number of training samples. But little research has been done to evaluate SVM’s performance for geographical object-based image analysis (GEOBIA). GEOBIA also allows the easy integration of additional features into the classification process, a factor which may significantly influence classification accuracies. As such, two experiments were developed and implemented in this research. The first compared the performances of object-based SVM, maximum likelihood (ML) and nearest neighbour (NN) classifiers using varying training set sizes. The effect of feature dimensionality on classifier accuracy was investigated in the second experiment. A SPOT 5 subscene and a four-class classification scheme were used. For the first experiment, training set sizes ranging from 4-20 per land cover class were tested. The performance of all the classifiers improved significantly as the training set size was increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training set sizes although ML achieved competitive results for sets of 12 or more training samples per class. Training sets were kept constant (20 and 10 samples per class) for the second experiment while an increasing number of features (1 to 22) were included. SVM consistently produced superior classification results. SVM and NN were not significantly (negatively) affected by an increase in feature dimensionality, but ML’s ability to perform under conditions of large feature dimensionalities and few training areas was limited. Further investigations using a variety of imagery types, classification schemes and additional features; finding optimal combinations of training set size and number of features; and determining the effect of specific features should prove valuable in developing more costeffective ways to process large volumes of satellite imagery. KEYWORDS Supervised classification, land cover, support vector machine, nearest neighbour classification maximum likelihood classification, geographic object-based image analysis