U_EFF_NET: High-Precision Segmentation of Offshore Farms From High-Resolution SAR Remote Sensing Images

Abstract

Offshore aquaculture promotes the development of aquaculture industry and brings huge economic benefits to fishermen, while seriously affecting the near-coast environment. Accurate access to the range of offshore farms at home and abroad is of great significance for marine disaster warning and coastal management. Remote sensing is a very effective means of observing offshore farms. Offshore farms segmentation technology is more mature in high-resolution optical images. SAR images have the advantage of being available all day and all night. Using SAR images to extract offshore farms has become a recent research hotspot. Gaofen-3 and HISEA-1 satellites have high-resolution marine observation capabilities. Combined with their super observation capabilities, this article proposes a technical solution to extract offshore farms using Gaofen-3 and HISEA-1 images. First, the combine-crop component module is proposed to create a new SAR images dataset based the original dataset. Next, common data augmentation methods, such as flip, rotate, and copy–paste, are used, and the online multiscale resolution image expansion is proposed to enrich the dataset and enlarge the data volume. Finally, the U_EFF_NET model is proposed. The model uses encoder–decoder structure and the light-weight feature extraction network EfficientNet-b0 as backbone. The decoder embeds the attention mechanism. The optimization strategy uses joint loss function to achieve multitask learning, and the OHEM strategy is added to the joint loss function. The method proposed in this article has high accuracy and high practicality as the frequency-weight intersection ratio of the extracted results on the test set is 98.12%. And the inference time is at the forefront.

Keywords

• 2021 GaoFen challenge
• high-resolution SAR remote sensing images
• offshore farms segmentation
• U_EFF_NET