Wave-Net: A Marine Raft Aquaculture Area Extraction Framework Based on Feature Aggregation and Feature Dispersion for...

Monitoring raft aquaculture areas plays an important role in the sustainability of marine aquaculture. With the advantages of full-time observation and ability to penetrate clouds, synthetic aperture radar (SAR) imaging has replaced laborious on-site investigation and has become the preferred approach. However, the existing deep learning-based semantic segmentation approaches generally suffer from speckle noise and have difficulty with multi-scale structures, which blurs the boundaries of raft aquaculture areas, and therefore, they connect them incorrectly. To cope with this problem, a wave-shaped neural network (Wave-Net), which is mainly composed of a feature aggregation part and a feature dispersion part, was proposed. Its feature aggregation part extracts both global and local features from different scales of raft aquaculture areas with asymmetric V-shaped subnetworks. Then, its feature dispersion part uses asymmetric Ʌ-shaped subnetworks to refine the boundaries of different scales of raft aquaculture areas. During these processes, both residual connections and reconstruction losses are adopted between the identical scales of feature maps to promote feature fusion and parameter optimization. The experimental results revealed that the proposed Wave-Net model solved the issue of blurred boundaries and achieved better segmentation accuracy with limited samples.