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ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 12 - 2024 |
doi: 10.3389/fenvs.2024.1443512
This article is part of the Research Topic New Artificial Intelligence Methods for Remote Sensing Monitoring of Coastal Cities and Environment View all articles
Research on Coastline Extraction and Dynamic Change from Remote Sensing Images Based on Deep Learning
Provisionally accepted- 1 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
- 2 Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing, China
Accurate coastline extraction is crucial for the scientific management and protection of coastal zones. Due to the diversity of ground object details and the complexity of terrain in remote sensing images, the segmentation of sea and land faces challenges such as unclear segmentation boundaries and discontinuous coastline contours. To address these issues, this study improve the accuracy and efficiency of coastline extraction by improving the DeeplabV3+ model. Specifically, this study constructs a sea-land segmentation network, DeepSA-Net, based on strip pooling and coordinate attention mechanisms. By introducing dynamic feature connections and strip pooling, the connection between different branches is enhanced, capturing a broader context. The introduction of Coordinate Attention allows the model to integrate coordinate information during feature extraction, thereby allowing the model to capture longer-distance spatial dependencies. Experimental results has shown that the model can achieves a land-sea segmentation mean Intersection over Union (mIoU) ration and Recall of over 99% on all datasets. Visual assessment results show more complete edge details of sealand segmentation, confirming the model's effectiveness in complex coastal environments. Finally, using remote sensing data from a coastal area in China as an application instance, coastline extraction and dynamic change analysis were implemented, providing new methods for the scientific management and protection of coastal zones.
Keywords: Sea-land segmentation, Remote sensing images, deep learning, Strip pooling, Coordinate attention
Received: 04 Jun 2024; Accepted: 02 Sep 2024.
Copyright: © 2024 Lv, Wang, Ge, Guan, Lu and Tao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zui Tao, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing, China
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