  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.
