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Ocean internal wave is a common and significant ocean phenomenon that occurs between different density layers in the ocean. In this study, 92 Sentinel-1 images containing internal solitary waves, collected from 2015 to 2019, were used. Through preprocessing and visual interpretation, label data were generated to form a high-quality dataset containing 4 608 pairs of sample images. To address the inadequacy in the research of multi-scale internal solitary wave feature extraction, this paper proposes a multiscale internal solitary wave feature extraction model based on the Swin-Unet network to improve the extraction capability for internal solitary waves at different scales. The performance evaluation results show that, compared to the U-Net method, the proposed Swin-Unet model improved by 2.3% in F1 score, 2.44% in precision, and 12.12% in mean intersection over union (mIoU). The internal solitary wave extraction results at different scales in the Andaman Sea were subsequently analyzed, and the improved model was applied to Sentinel-1 full-track SAR imagery, verifying its applicability and robustness in complex marine regions. Experimental results demonstrate that the proposed model, based on the constructed dataset, can automatically extract ocean internal solitary wave features from SAR images.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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