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Accurate extraction of marine raft aquaculture area information is of great significance for marine resource management and environmental monitoring. But the raft culture area is often submerged in water with weak data signal areas, resulting in low extraction accuracy based on optical images alone. Therefore, this paper takes Weihai Rongcheng Bay as the research area, and improves the U-Net neural network by adding channel attention mechanism to combine the spectral information of GF-2 optical image and the texture information of GF-3 radar image, trying to improve the extraction accuracy of raft aquaculture area. The results show that: （1) Whether it is a single optical image or a fusion image of optical and radar images, the overall accuracy of the prediction results of the U-Net neural network with channel attention mechanism will be improved, with an increase of 2.21%−4.12%. （2) Using the improved U-Net neural network to process the fusion data, the overall accuracy is 95.75%, which is 4.3% higher than that of only using GF-2 image. （3) For weak signal region, the overall accuracy and Kappa coefficient of extraction based on improved network and data fusion are 91.61% and 0.827 7, respectively. This method can effectively extract the weak signal area of marine raft aquaculture area, and can provide technical support for marine aquaculture area statistics and marine environment detection.
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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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