Inshore aquaculture pond extraction algorithm based on global context fusion
Aiming at the problems that the existing methods are not effective in distinguishing aquacul-ture ponds and interfering objects,and the universality of multi-source high-resolution remote sensing images needs to be verified,a PG-Unet aquaculture pond extraction model integrating global context information is proposed.On the basis of U-Net,the model captures rich global context information by adding pyramid feature extraction unit,and increases global guiding flow to improve the quality of feature maps at different levels,so as to improve the ability of the model to locate targets in multi-interference environment.The experimental results on GF-2 PMS and BJ-2 PMS datasets show that the PG-Unet model has the best accuracy,and its IoU and F1 scores reach 92.30％,96.00％and 92.07％,95.87％,respectively,which are better than U-Net,DensenetUnet and U2Net.It has stronger anti-interference ability and universality,and can better distinguish aquaculture ponds and disturbed objects.At the same time,the application of PG-Unet model in Zhao'an Bay aquaculture area has also achieved high extraction accuracy,which can realize the automatic and accurate extrac-tion of spatial distribution information of large-scale aquaculture ponds.
万方数据
维普
