# A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

arXiv: https://arxiv.org/abs/2003.07784 · HTML: https://mlsanity.com/paper/2003.07784/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline](https://mlsanity.com/paper/2103.01849/) (2021)
- [Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery](https://mlsanity.com/paper/2106.00880/) (2021)
- [Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2108.08063/) (2021)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [Challenges in data-based geospatial modeling for environmental research and practice](https://mlsanity.com/paper/2311.11057/) (2023)
- [Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset](https://mlsanity.com/paper/2409.15311/) (2024)
- [Coastline extraction from ALOS-2 satellite SAR images](https://mlsanity.com/paper/2104.04722/) (2021)

## Abstract (arXiv)

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.