1.Yazhou Bay Innovation Institute，Hainan Tropical Ocean University，Sanya 572025，China；2.Hainan Academy of Ocean and Fisheries Sciences，Haikou 571126，China；3.School of Surveying and Land Information Engineering，Henan Polytechnic University，Jiaozuo 454003，China；4.School of Remote Sensing and Information Engineering，North China Institute of Aerospace Engineering，Langfang 065000，China
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Published
2024-05-21
Issue Date
2024-05-21
Abstract
In order to solve the problem of low accuracy in extracting aquaculture areas using remote sensing technology due to complex background of onshore aquaculture and offshore cage culture areas and to easily disturbed by factors including houses, vegetation, seawater, and ships, a complex deep learning method that combines shuffle attention mechanisms and spatial transformation network was proposed, and tested in Bamen bay in Wenchang city and Potou Port in Wanning City. With the help of GF-2 high resolution remotely sensed data, the prior knowledge of aquaculture targets was constructed using spectral and texture features. Then, based on the U-Net model, the spatial transformation network (STN) and the shuffle attention (SA) mechanism are combined to enhance the spatial characteristics of the aquaculture area and to reduce the interference of complex backgrounds. The test results showed that the overall accuracy and mean intersection over union of SA-STN-Net model were enhanced by 3.3% and 5.7% compared with the preliminary U-Net model, respectively. Swin-Transformer, Dc-Swin, and F1 score of SA-STN-Net model were found to be increased by 6.7%, 4.2% and 7.2% in the score compared with the most state-of-art deep learn model such as A2fpn, respectively. The findings demonstrate that the proposed SA-STN-Net model is adapted to the complex environmental background of offshore aquaculture, effectively extracts offshore aquaculture targets, and can provide technical support for offshore planning and management departments.
WU Tongren, ZHANG Xian, LIU Pei, WEN Tingting , ZOU Zhenxue.
An information extraction method from offshore aquaculture area based on spatial transformation and shuffle attention mechanism[J]. Journal of Dalian Fisheries University, 2024, 39(2): 327-336 https://doi.org/10.16535/j.cnki.dlhyxb.2023-230
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