Extraction of Floating Raft Aquaculture Information Based on U-Net Neural Network——A Case Study of Changhai County
Floating raft aquaculture is one of the most important types of marine aquaculture,so accurate extrac-tion of floating raft cultivation is particularly important.However,floating rafts are densely distributed in remote sensing images and have complex backgrounds.Previous methods for extracting floating rafts mainly rely on ma-chine learning,which fail to explore the deep features of floating raft cultivation and efficiently utilize spectral in-formation.To address these issues,a U-Net network model for extracting floating raft aquaculture information is proposed.Ratio indices are used to calculate feature bands,remove redundant spectral information,and add a deep neural network to deepen the extraction of deep feature channels for floating raft information,achieving high-pre-cision extraction of floating raft aquaculture information.Dachangshan Island in Changhai County is selected as the study area and the extraction results with Canny operator,Otsu algorithm,and PCA_Kmeans algorithm is compared.The overall accuracy of floating raft aquaculture extraction using the U-Net model is 95.6％,which is 9％to 13％higher than common machine learning algorithms,demonstrating the efficiency of the U-Net model in identifying floating raft aquaculture.
floating raft aquacultureU-Net modelratio indexdeep featurespectral information
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