Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and cos...

The aim of this study is to introduce a new methodology for isolation of ectopic rhythms of ambulatory electrocardiogram (ECG) holter data via appropriate statistical analyses imposing reasonable computational burden. First, the events of the ECG signal are detected and delineated using a robust wavelet-based algorithm. Then, using Binary Neyman-Pearson Radius test, an appropriate classifie...

This paper addresses a classification problem in which class definition through training samples or otherwise is provided a priori only for a particular class of interest. Considerable time and effort may be required to label samples necessary for defining all the classes existent in a given data set by collecting ground truth or by other means. Thus, this problem is very important in practice,...

In this study, land cover types in Zonguldak test area were analysed on the basis of the classification results acquired using the pixelbased and object-oriented image analysis approaches. Landsat-7 ETM with 6 spectral bands was used to carry out the image classification and ground truth data were collected from the available maps, aerial photographs, personal knowledge and communication with t...

A wide range of non-parametric classifiers have been suggested and developed in the recent years in order to overcome the compulsion of using the classical parametric Maximum Likelihood Classifier (MLC) for non-normal data. The most advanced of these classifiers being the ones based on the Artificial Neural Network (ANN) algorithm, the Support Vector Machines (SVMs) and the Random Forests (RFs)...

To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...