Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Journal of Intelligent and Robotic Systems
IEEE Transactions on Neural Networks
Comparison of two different PNN training approaches for satellite cloud data classification
IEEE Transactions on Neural Networks
A temporally adaptive classifier for multispectral imagery
IEEE Transactions on Neural Networks
Using uncertainty information to combine soft classifications
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Hi-index | 0.00 |
Standard methodologies for estimating the thematic accuracy of hard classifications, such as those using the confusion matrix, do not provide indications of where thematic errors occur. However, spatial variation in thematic error can be a key variable affecting output errors when operations such as change detection are applied. One method of assessing thematic error on a per-pixel basis is to use the outputs of a classifier to estimate thematic uncertainty. Previous studies that have used this approach have generally used a single classifier and so comparisons of the relative accuracy of classifiers for predicting per-pixel thematic uncertainty have not been made. This paper compared three classification methods for predicting thematic uncertainty: the maximum likelihood, the multi-layer perceptron and the probabilistic neural network. The results of the study are discussed in terms of selecting the most suitable classifier for mapping land cover or predicting thematic uncertainty.