Evaluating Different Deep Learning Models for Automatic Water Segmentation

Deep learning (DL) methods, integrated with imagery obtained by remote sensors, are considered a novel source of information in the field of hydrometry. Their results can be a support for standard gauging systems. In this research, three different model definitions based on the SegNet architecture were developed: training a new model, using a pre-trained model, and performing transfer learning. The main goal of this approach is the performance assessment of convolutional neural network (CNN) generalization to segment images containing different water bodies automatically. Diverse sensors were used to obtain RGB images from different areas of the world. The effectiveness of the CNN was estimated using pixel accuracy and IoU metrics. Training a new model and using transfer learning revealed similar high accuracies that were at least twice as accurate compared to the pre-trained model. However, the transfer learned model is preferred due to significantly lower training expenses.