Abstract:

Classification maps are one of the main output types of environmental monitoring tasks. In this study, deep learning algorithms were applied using remote sensing data for image classification. In the application, different CNN models were used on two data sets, UC Merced and WHU-RS19. In the test phase, multi-class classification was made with the predictions of deep learning models and the evaluation criteria of the classification were calculated. The performances of the CNN models used in the data sets were evaluated in the overall accuracy metric. It has been observed that the DenseNet201 model has higher performance results in tests on both the UC Merced and WHU-RS19 datasets. The results obtained were compared with the results of other studies in the literature. It has been observed that the DenseNet201 model used in this study has higher performance than other studies with an overall accuracy of 98.81% in the application in the UC Merced dataset. In addition, land use classes that are similar in both data sets were determined and the results of the best performing algorithm were interpreted. Classification of similar classes in the tests was evaluated using the evaluation metrics of precision, recall and F1 score.

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