  Sea-land segmentation is an important process for many key applications in
remote sensing. Proper operative sea-land segmentation for remote sensing
images remains a challenging issue due to complex and diverse transition
between sea and lands. Although several Convolutional Neural Networks (CNNs)
have been developed for sea-land segmentation, the performance of these CNNs is
far from the expected target. This paper presents a novel deep neural network
structure for pixel-wise sea-land segmentation, a Residual Dense U-Net
(RDU-Net), in complex and high-density remote sensing images. RDU-Net is a
combination of both down-sampling and up-sampling paths to achieve satisfactory
results. In each down- and up-sampling path, in addition to the convolution
layers, several densely connected residual network blocks are proposed to
systematically aggregate multi-scale contextual information. Each dense network
block contains multilevel convolution layers, short-range connections and an
identity mapping connection which facilitates features re-use in the network
and makes full use of the hierarchical features from the original images. These
proposed blocks have a certain number of connections that are designed with
shorter distance backpropagation between the layers and can significantly
improve segmentation results whilst minimizing computational costs. We have
performed extensive experiments on two real datasets Google Earth and ISPRS and
compare the proposed RDUNet against several variations of Dense Networks. The
experimental results show that RDUNet outperforms the other state-of-the-art
approaches on the sea-land segmentation tasks.
