Remote sensing has become a primary technology for monitoring raft aquaculture products. However, due to the complexity of the marine aquaculture environment, the boundaries of the raft aquaculture areas in remote sensing images are often blurred, which will result in ‘adhesion’ phenomenon in the raft aquaculture areas extraction. The fully convolutional network (FCN) based methods have made great progress in the field of remote sensing in recent years. In this paper, we proposed an FCN-based end-to-end raft aquaculture areas extraction model (which is called UPS-Net) to overcome the ‘adhesion’ phenomenon. The UPS-Net contains an improved U-Net and a PSE structure. The improved U-Net can simultaneously capture boundary and contextual information of raft aquaculture areas from remote sensing images. The PSE structure can adaptively fuse the boundary and contextual information to reduce the ‘adhesion’ phenomenon. We selected laver raft aquaculture areas in eastern Lianyungang in China as the research region to verify the effectiveness of our model. The experimental results show that compared with several state-of-the-art models, the proposed UPS-Net model performs better at extracting raft aquaculture areas and can significantly reduce the ‘adhesion’ phenomenon.

The rapid development of factory aquaculture not only brings economic benefits to coastal areas but also poses numerous ecological and environmental challenges. Therefore, understanding the distribution of coastal factory aquaculture is of great significance for ecological protection. To tackle the issue of the complex spectral and spatial characteristics in remote-sensing images of different factory aquaculture plants in coastal areas, a multiscale residual attention network (MRAN) model for extracting factory aquaculture information is proposed in this study. MRAN is a modification of the U-Net model. By introducing a residual structure, an attention module, and a multiscale connection MRAN can solve the problem of inadequately detailed information extraction from a complex background. In addition, the coastal areas of Huludao City and Dalian City in Liaoning Province were selected as the research areas, and experiments were conducted using the domestic Gaofen-1 remote-sensing image data. The results indicate that the pixel accuracy (PA), mean PA, and mean intersection over union of the proposed model are 98.31%, 97.85%, and 92.46%, respectively, which are superior to those of other comparison models. Moreover, the proposed model can effectively reduce misidentification and missing identification phenomena caused by complex backgrounds and multiple scales.