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An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, for remote sensing images with rich details and different target scales, there are still some problems such as insufficient segmentation accuracy. To handle these problems, we propose an island remote sensing image segmentation algorithm based on FC_U-Net network. In this network, U-Net and FCN (Fully Convolutional Network) are improved respectively to form two new modules, and then the semantic features and detail features from the two modules are fused by a fusion module. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of FC_U-Net are better than other three state-of-the-art methods, U-Net, FCN (Fully Convolutional Network), SegNet, which indicates the effectiveness of our method.