A Tutorial on Classification of Remote Sensing Data
نویسنده
چکیده
Image classification is a well known of the significant tools used to recognize and examine most sharp information in satellite images. In any remote sensing research, the decision-making way mainly rely on the efficiency of the classification process. There are disparate classification algorithms on the large satellite imagery: Multilayer perceptron back propagation neural network (MLP BPNN), Support vector machine (SVM), kmeans, Cluster ensemble based (CEB) method, Unsupervised Deep Feature learning (UDFL), Semisupervised Ensemble Projection (SSEP). We discussed different performance measures such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, to know the performance of each classifier.
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