WATER BODY DETECTION BASED ON AN IMPROVED U-NET

Nowadays, deep learning has been widely used for water body detection because of its high precision data-based water segmentation ability. Although the networks based on deep learning have shown higher automation, applicability and extraction accuracy than the traditional threshold methods in water body detection, only the translation equivariance of the convolution kernel is considered in these networks. Actually, for the detection of water body, its rotation equivariance also needs to be considered. For this goal, we here propose a new convolutional neural network by improving the U-Net with the rotation equivariant convolution and attention mechanism, which is simplified as GACNN. Experimental results on optical water body images demonstrate the effectiveness of the improved network based on U-net.