MEASURING CLASSIFICATION ACCURACIES USING IMPROVED THERMAL INFRARED DATA
Keywords:Artificial Neural Networks, Classification Accuracy, Maximum Likelihood Approach, Thermal Infrared Data
AbstractImage classification is one of the major aspects in digital image analysis of remotely sensed data. In this paper, we present the effects on classification accuracy if improved thermal data are used instead of raw thermal data. We use two methods, Artificial Neural Networks (ANN) and Maximum Likelihood Approach (MLH) to demonstrate our purpose. Using each method different combinations of raw and improved data are tested to classify in order to compare the accuracies. As a final note, the findings are discussed.
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