DeepUNet：一种用于像素级海陆分割的深度全卷积网络
DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation
摘要：
语义分割是遥感图像处理中的一项基础性研究。由于海洋环境的复杂性，海陆分割是一项具有挑战性的任务。虽然近年来神经网络在语义分割方面取得了很好的效果，但将CNN用于海陆分割的研究还不多，其效果有待进一步提高。本文提出了一种新的深度卷积神经网络DeepUNet。像U-Net一样，它的结构有一个收缩路径和一个扩展路径，以获得高分辨率的输出。但不同的是，DeepUNet在收缩路径中使用DownBlock而不是卷积层，在扩展路径中使用UpBlock。这两个新的块带来了两个新的连接，即U连接和Plus连接。它们被提升以获得更精确的分割结果。为了验证我们的网络架构，我们制作了一个新的具有挑战性的海陆数据集，并将DeepUNet与SegNet和U-Net进行了比较。实验结果表明，与其他架构相比，DeepUNet取得了良好的性能，尤其是在高分辨率遥感图像中。
英文摘要：
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high resolution output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify our network architecture, we made a new challenging sea-land dataset and compare the DeepUNet on it with the SegNet and the U-Net. Experimental results show that DeepUNet achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
