DBENet: Dual-Branch Ensemble Network for Sea-Land Segmentation of Remote-Sensing Images

Sea-land segmentation of optical remote-sensing images holds great importance for military and civilian applications, such as coastal monitoring, target detection, and resource management. Although convolutional neural networks (CNNs) have achieved significant improvements in semantic segmentation, the challenges of efficient feature extraction, representation, fusion, and information transmission remain unsolved, which especially impacts the segmentation effectiveness of existing CNN-based models in extracting irregular and refined sea-land boundaries. In this article, a novel dual-branch ensemble network (DBENet) is proposed for pixel-level sea-land segmentation. The salient properties of the DBENet are: 1) a novel dual-branch network architecture is developed to achieve sufficient feature extraction and representation and 2) an efficient ensemble attention learning strategy suitable for the DBENet is designed to strengthen the correlation between dual branches to further facilitate feature fusion and information transmission. The comparative study with state-of-the-art methods reveals the superior performance of our approach, and the ablation study demonstrates the effectiveness of each component in the proposed network. The source code is available at https://github.com/RobertTang0/DBENet.

Automated summary

This article introduces a novel dual-branch ensemble network (DBENet) for pixel-level sea-land segmentation, addressing the challenges of extracting irregular and refined sea-land boundaries. DBENet achieves sufficient feature extraction and representation through a dual-branch network architecture, and enhances feature fusion and information transmission through an efficient ensemble attention learning strategy. Comparative study demonstrates the superior performance of our approach, and ablation study validates the effectiveness of each component in the proposed network.