| Autor: |
SHENG Sheng, WAN Fangqi, LIN Kangling, HU Zhaoyang, CHEN Hua |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Renmin Zhujiang, Vol 45, p (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1001-9235 |
| DOI: |
10.3969/j.issn.1001-9235.2024.02.006 |
| Popis: |
Highly accurate water body extraction can be helpful for water resources monitoring and management.The current methods of water body extraction based on remote sensing images lack attention to boundary quality,resulting in inaccurate boundary delineation and low detail retention.To improve the boundary and detail accuracy of water body extraction for remote sensing images,this paper proposes a deep learning algorithm for water body extraction from high-resolution remote sensing images based on multi-scale feature fusion.The model includes a hierarchical feature extraction module and a stacked-connected decoder module that fuses multi-scale features.In the hierarchical feature extraction module,a channel attention structure is introduced for integrating shape,texture,and hue information of water bodies in high-resolution remote sensing images to better understand the shapes and boundaries of water bodies.In the stacked-connected decoder module that incorporates multi-scale features,the stacked connection of multi-level semantic information and enhanced feature extraction are performed.Meanwhile,broad background information and fine detail information are captured to achieve better water body extraction results.Experimental results on both self-annotated and publicly available datasets show that the model yields 98.37% and 91.23% accuracy,and extracts more complete edges of water bodies while retaining more details than existing semantic segmentation models.The proposed model improves the accuracy and generalization ability of water body extraction and provides references for water body extraction from high-resolution remote sensing images. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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