| Autor: |
LI Ning, NIU Shilin |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Leida xuebao, Vol 9, Iss 1, Pp 174-184 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2095-283X |
| DOI: |
10.12000/JR19096?viewType=HTML |
| Popis: |
The extraction of water from Synthetic Aperture Radar (SAR) images is of great significance in water resources investigation and monitoring disasters. To deal with the problems of the insufficient accuracy of water boundaries extracted from middle-low resolution SAR images. This paper proposes a high-precision water boundaries extraction method based on a local super-resolution restoration technology that combines the advantages of the super-resolution restoration technology based on the lightweight residual Convolutional Neural Network (CNN) and the traditional SAR images water extraction methods. The proposed method can significantly improve the accuracy of water segmentation results by using SAR images. To verify the effectiveness of the proposed method, as a study area, we selected the Danjiangkou Reservoir, the water source of the middle route of a south-to-north water diversion project. Further, we conducted experiments on the multi-mode SAR dataset and evaluated its accuracy. This dataset included one Standard Strip-map (SS) mode image obtained by the Chinese GaoFen-3 (GF-3) satellite with a resolution of 8 m and one Interferometric Wide-swath (IW) mode SAR image obtained by Sentinel-1 satellite with a resolution of 20 m. The experimental results showed that the water segmentation results from the middle–low resolution SAR images of the proposed method were more precise, and the overall water segmentation performance was superior to that of the traditional methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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