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The paper presents a method of predicting classification accuracy of remote sensing data by means of training set analysis. Various sampling plans were applied to satellite image and its complete ground truth to derive different training sets. The quality of these training sets was determined by quantifying the similarity of the training set distributions to the ones of the entire satellite image. Each training set was then used to learn a classifier. The paper shows how the accuracy of classifications that were carried out using these classifiers depends upon the quality of the corresponding training sets.

Accuracy Assessment of Satellite Image Classification Depending on Training Sample