SAR (sentetik açıklıklı radar) görüntülerinde bölütleme
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Date
2021
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Fen Bilimleri Enstitüsü
Abstract
The aim of this thesis are to explain of the processes of creating a new data set with SAR (Synthetic Aperture Radar) images in detail, to demonstrate of how this new dataset is used with deep learning models and to compare of these models used with Mc Nemar's test. Although SAR images can be accessed freely and easily, these are not convenient to be used directly due to the speckle noise, and also there is almost no free available labeled dataset for scientific research. In this study, we propose a novel process that automatedly creates a dataset and removes the speckle noise, labeling images and using the automatedly-created dataset to enhance semantic segmentation task results with state of the art deep neural networks. Used 3 models are evaluated with Mc Nemar's test. As a result, we achieved an overall pixel accuracy (PA) of 92.23% and a mean Intersection over Union (mIoU) of 70.60%. Beside, to show the effectiveness of our noise removal process, we compare the results of models on speckled noise and noise-free versions of our newly-created dataset.
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SAR