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Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure

1
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2
National Marine Environmental Monitoring Center, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2053; https://doi.org/10.3390/rs11172053
Received: 5 August 2019 / Revised: 28 August 2019 / Accepted: 30 August 2019 / Published: 1 September 2019
(This article belongs to the Section Ocean Remote Sensing)
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. View Full-Text
Keywords: raft aquaculture areas extraction; remote sensing image; fully convolution network; U-Net; pyramid upsampling; squeeze-excitation module raft aquaculture areas extraction; remote sensing image; fully convolution network; U-Net; pyramid upsampling; squeeze-excitation module
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MDPI and ACS Style

Cui, B.; Fei, D.; Shao, G.; Lu, Y.; Chu, J. Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure. Remote Sens. 2019, 11, 2053. https://doi.org/10.3390/rs11172053

AMA Style

Cui B, Fei D, Shao G, Lu Y, Chu J. Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure. Remote Sensing. 2019; 11(17):2053. https://doi.org/10.3390/rs11172053

Chicago/Turabian Style

Cui, Binge, Dong Fei, Guanghui Shao, Yan Lu, and Jialan Chu. 2019. "Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure" Remote Sensing 11, no. 17: 2053. https://doi.org/10.3390/rs11172053

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