A sea-land segmentation method combining contextual semantic information and edge features

doi:10.6046/zrzyyg.2024286

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Abstract

In optical remote sensing images with complex scenes and rich land cover information，the sea-land segmentation faces challenges such as low positioning accuracy and blurred edges. Therefore，this paper proposed a deep convolutional network model and a sea-land segmentation method that integrate contextual semantic information and edge features. First，the rich target semantic information was extracted from remote sensing images using the FusionNet semantic segmentation network module. Then，multi-scale and hierarchical contextual semantic features were extracted from the segmentation network using the enhanced atrous spatial pyramid pooling （ASPP） module and contextual attention module. Additionally，an edge extraction sub-network was built to extract multi-scale edge features. Finally，the semantic features and edge features were combined through a fusion module，thereby achieving accurate sea-land segmentation. This method was tested with two typical representative datasets. The results showed that this method achieved an overall prediction accuracy of 98.21%，an F1 score of 97.64%，and a boundary F1 score of 89.36%，all significantly outperforming other models. Particularly in complex backgrounds，this method can effectively improve the accuracy of segmentation and edge detection，demonstrating definite advantages in the segmentation of artificial coastlines and ports.

Keywords sea-land segmentation edge extraction semantic segmentation multi-task learning contextual attention module

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TP79

Issue Date: 28 October 2025

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	Tiantian WEN
	Yunwei PU
	Wenxiang ZHAO

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Tiantian WEN,Yunwei PU,Wenxiang ZHAO. A sea-land segmentation method combining contextual semantic information and edge features[J]. Remote Sensing for Natural Resources, 2025, 37(5): 62-72.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024286 OR https://www.gtzyyg.com/EN/Y2025/V37/I5/62

Fig.1 Structure of ES-Net model

Fig.2 Semantic segmentation module

Fig.3 ASPP module diagram

Fig.4 Convolutional block attention module

Fig.5 Improved spatial attention mechanism

Tab.1 Detailed configuration of the edge extraction network

Fig.6 Edge extraction module

Fig.7 Sample images from the Coastline-Segmentation dataset

Fig.8 Sample images from the HRSC2016 dataset

Tab.2 Segmentation results of different methods on the Coastline-Segmentation dataset

Tab.3 Segmentation results of different methods on the HRSC2016 dataset

Tab.4 Comparative study of segmentation performance across different networks （%）

Fig.9 Comparison of ablation experiment results

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