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Extraction of Aquaculture Ponds along Coastal Region Using $U^{2}$-Net Deep Learning Model from Remote Sensing Images
| Content Provider | MDPI |
|---|---|
| Author | Zou, Zhaohui Chen, Chao Liu, Zhisong Zhang, Zili Liang, Jintao Chen, Huixin Wang, Liyan |
| Copyright Year | 2022 |
| Description | The main challenge in extracting coastal aquaculture ponds is how to weaken the influence of the “same-spectrum foreign objects” effect and how to improve the definition of the boundary and accuracy of the extraction results of coastal aquaculture ponds. In this study, a recognition model based on the $U^{2}$-Net deep learning model using remote sensing images for extracting coastal aquaculture ponds has been constructed. Firstly, image preprocessing is performed to amplify the spectral features. Second, samples are produced by visual interpretation. Third, the $U^{2}$-Net deep learning model is used to train and extract aquaculture ponds along the coastal region. Finally, post-processing is performed to optimize the extraction results of the model. This method was validated in experiments in the Zhoushan Archipelago, China. The experimental results show that the average F-measure of the method in the study for the four study cases reaches 0.93, and the average precision and average recall rate are 92.21% and 93.79%, which is suitable for extraction applications in aquaculture ponds along the coastal region. This study can quickly and accurately carry out the mapping of coastal aquaculture ponds and can provide technical support for marine resource management and sustainable development. |
| Starting Page | 4001 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14164001 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-17 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing U2-net Deep Learning Remote Sensing Image Object Detection Coastal Aquaculture Ponds |
| Content Type | Text |
| Resource Type | Article |
