Research on information extraction of cage aquaculture in river-type reservoir based on multi-scale feature fusion
In order to develop a method for extracting information on cage aquaculture suitable for river-type reservoirs and achieve automated and accurate extraction of cage aquaculture information in reservoirs,this article improves the construction of a deep learning network based on the"U"type encoding and decoding structure,taking into account multi-scale feature information.By introducing modules such as Residual Unit(RU),Efficient Multi-Scale Attention(EMA),improved cascade multi-scale convolution(MCP),and embedded multi-scale features(IAC),to construct the EAMRNet model.Taking the Shuikou reservoir area of Minjiang River Basinn as the research area,the study on extracting information on cage aquaculture in reservoirs was conducted.The results showed that the IoU,Recall,Precision,and F1-score of the EAMRNet model were 80.26％,90.94％,87.23％,and 89.05％,respectively.Compared with the accuracy evaluation results of five classic models such as UNet,ResUNet,DeepLab V3+,TransUNet,and HRNet,the accuracy was the highest.At the same time,the EAMRNet model was applied to extract cage aquaculture information from 2019 to 2023 in the Shuikou reservoir area of Minjiang River basin.According to the statistics of the extraction results,the cage aquaculture area in the Shuikou reservoir area of Minjiang River basin decreased from 333.965 2 hm2 in 2019 to 156.771 3 hm2 in 2023,showing a trend of first increasing and then decreasing.In summary,the improved model has high extraction accuracy for extracting information on cage aquaculture in reservoirs.This study can provide theoretical basis and data support for local aquaculture management departments to conduct dynamic monitoring and rational planning of aquaculture.
river type reservoirsmulti scale featuresreservoir cage aquaculturedeep learningdomestic high-resolution remote sensing images
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