Journal
REMOTE SENSING
Volume 11, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/rs11172053
Keywords
raft aquaculture areas extraction; remote sensing image; fully convolution network; U-Net; pyramid upsampling; squeeze-excitation module
Categories
Funding
- National key R&D Program of China [2017YFC1405600]
- National Natural Science Foundation of China (NSFC) [41406200]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available