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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,...
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...
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