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Accurate land use/cover (LUC) classifications from satellite imagery are very important for eco-environment monitoring, land use planning and climatic change detection. Traditional statistical classifiers such as minimum distance (MD) have been used to extract LUC classifications in urban areas, but these classifiers rely on assumptions that may limit their utilities for many datasets. On the contrary,...
This paper presents a new classification method based on a combination of GIS and BP (back propagation) artificial neural network, taking the TM image of the area of Jinzhou city in 2000 as the testing one. BP neural network can be optimized by means of selecting GIS data aided training sample, improving training algorithm, calculating the number of nodes in the hidden layer and so on. Compared with...
The artificial neural network (ANN) is a popular nonparametric approach for supervised classification. ANN has been extensively applied to perform classification of remotely sensed data in this paper because it has been shown to be able to map land cover more accurately than the widely used statistical classification techniques. This study presents a back-propagation neural network (BPN), which is...
Logistic regression (LR) has become a widely used and accepted method to analyse binary or multiclass outcome variables, since it is a flexible tool that can predict the probability for the state of a dichotomous variable. A recently proposed LR method is based on the hybridisation of a linear model and evolutionary product-unit neural network (EPUNN) models for binary classification. This produces...
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