RaftNet: A New Deep Neural Network for Coastal Raft Aquaculture Extraction from Landsat 8 OLI Data

Abstract

The rapid development of marine ranching in recent years provides a new way of tackling the global food crisis. However, the uncontrolled expansion of coastal aquaculture has raised a series of environmental problems. The fast and accurate detection of raft will facilitate scientific planning and the precise management of coastal aquaculture. A new deep learning-based approach called RaftNet is proposed in this study to extract the coastal raft aquaculture in Sansha Bay using Landsat 8 OLI images accurately. To overcome the issues of turbid water environments and varying raft scales in aquaculture areas, we constructed the RaftNet by modifying the UNet network with dual-channel and residual hybrid dilated convolution blocks to improve the extraction accuracy. Meanwhile, we adopted the well-known semantic segmentation networks (FCN, SegNet, UNet, UNet++, and ResUNet) as the contrastive approaches for the extraction. The results suggested that the proposed RaftNet model achieves the best accuracy with a precision of 84.5%, recall of 88.1%, F1-score of 86.30%, overall accuracy (OA) of 95.7%, and intersection over union (IoU) of 75.9%. We then utilized our RaftNet to accurately extract a raft aquaculture area in Sansha Bay from 2014 to 2018 and quantitatively analyzed the change in the raft area over this period. The results demonstrated that our RaftNet is robust and suitable for the precise extraction of raft aquaculture with varying scales in turbid coastal waters, and the Kappa coefficient and OA can reach as high as 88% and 97%, respectively. Moreover, the proposed RaftNet will unleash a remarkable potential for long time-series and large-scale raft aquaculture mapping.

Keywords

• raft aquaculture
• deep learning
• Landsat 8 OLI images
• RaftNet
• Sansha Bay