## 1 Introduction  
Artificial neural networks are widely used for supervised classification applications. In these applications, cases of known class membership are used to train the neural network in order to allow it to predict the class membership of previously unseen and unlabeled cases. This type of analysis is common in, for example, the production of thematic maps, such as those depicting land cover, from remotely-sensed imagery [1; 2; 3]. The imagery contain data on the remotely-sensed response of the land surface that is converted into information on land cover class via the classification analysis and a wide variety of approaches and applications have been investigated, e.g., [2; 4; 5]. Neural networks have become a popular method for image classification as numerous studies have shown that they can yield more accurate maps than a variety of other alternative approaches to classification [6; 7; 8]. The relative performance of neural network classifiers in relation to a range of alternative methods, including standard statistical classifiers, machine learning methods, and decision trees, is discussed in the literature (e.g., [1]). As with all classifiers, the quality of the final classification is, however, in part a function of the classifier and the nature, notably the size and quality, of the ground reference dataset on the class membership used [9; 10; 11; 12]. Ground reference data are used to provide data to train the neural network and to evaluate the quality of its predictions. The latter is typically expressed in terms of the overall accuracy of the classification output from the network. In remote sensing applications, the class membership of the cases in the training set is typically determined by ground-based observation or interpretation of very fine spatial resolution imagery [1; 13]. Clearly, the nature and quality of the ground reference data used will impact upon the accuracy of the predictions obtained from the neural network. Classification accuracy is, for example, influenced by the size, composition, and quality of the ground reference dataset used in training [2; 9; 10; 14; 15; 16]. The way the ground reference data are used is also important, especially if part of it is used for the purpose of validation [10]. The use of some reference data for validation purposes, to indicate the quality of the trained model generated, is commonplace in defining a supposedly optimal neural network. In common remote sensing applications, the optimal approach would be the one that yielded the most accurate thematic map when applied to the image data. In a typical remote sensing application, the aim of the classification is to accurately map the land cover classes in the region of study. Throughout there are a set of basic assumptions made in classification analyses. These include the need for the set of classes to be mutually exclusive and exhaustively defined. Failure to satisfy these assumptions will result in errors. Cases of an untrained class will, for example, typically have to be allocated erroneously to one of the defined classes [17]. Although there are instances when a neural network may be used to obtain a non-standard classification, such as soft classification when classes intergrade or are mixed together [17], the standard hard classification in which a case belongs fully to a single class is the focus on this article. In all cases, the ground reference data used in training, validating, and testing the neural network have a key role to play in the production of an accurate map. Hence, the design of these datasets is important. Good practices for classification accuracy assessment for the evaluation of the quality of thematic maps derived via remote sensing have been defined and include guidance on the construction of the testing sample upon which the assessment is to be based. Typically, for example, the use of a reference dataset acquired following a probabilistic sampling design is recommended [18; 19].