Mapping of lakes and heave mounds in the Arctic using synthetic aperture radar and interferometric synthetic aperture radar data with deep learning technologies

This paper deals with the process of developing and training a U-Net neural network for image segmentation of lakes and hillocks based on synthetic aperture radar and interferometric synthetic aperture radar data. The main goal of the work is to create an effective deep learning model capable of automatically identifying lakes and heave mounds based on complex radar images. To achieve this goal, several stages were carried out, including data collection and annotation, selection of the neural network architecture, training and validation of the model, as well as evaluation of its performance. At the beginning of the work, the process of creating a training dataset is described, which includes annotating images, highlighting features, and preparing data for training. Next, we consider the U-Net architecture, which was chosen because of its ability to efficiently segment objects in images. The choice of hyperparameters, such as the number of filters, the size of the convolution core and activation functions, is justified, and the Adam optimizer is used to achieve fast and stable convergence of the model. The learning and validation process of the model is described in detail with an emphasis on using the validation subset to monitor performance. Regularization methods, including early stopping, are used to prevent overfitting and improve the generalizing ability of the model. As a result, the importance of using deep learning for synthetic aperture radar and interferometric synthetic aperture radar data analysis is demonstrated, as well as confirmation of the effectiveness of the U-Net model for solving segmentation problems.

frost mounds, synthetic aperture radar, satellite image decoding, neural networks, deep learning, permafrost, cryolithozone, Arctic