MDOAU-Net: A Lightweight and Robust Deep Learning Model for SAR Image Segmentation in Aquaculture Raft Monitoring

Abstract

Offshore aquaculture raft information extraction from synthetic aperture radar (SAR) images is essential for large-scale marine resource exploitation and protection. In this letter, a deep learning model called multi-scaled attention U-net with dilated convolution and offset convolution (MDOAU-net) is proposed for aquaculture raft monitoring via SAR image segmentation. The U-net backbone and attention gate of the Attention U-net are used in the MDOAU-net model. In addition, the MDOAU-net model consists of three distinctive parts. First, a multi-scale feature-fusion block is adopted in its input to extract features from raw images. Moreover, adapted from the Attention U-net for SAR image segmentation, fewer channels are used in each convolution layer of the MDOAU-net to match latent features in SAR images. Furthermore, nine dilated convolution blocks are adopted in the encoder–decoder structure to extract semantic features in the presence of speckle noises. In addition, offset convolution blocks are developed to convert spatial information into channel information for the precise segmentation of blurry boundaries. Four skip connections of the U-net backbone are replaced by four offset convolution blocks. Experimental results are elaborated to demonstrate the superior performance of the MDOAU-net model to seven existing methods in terms of overall accuracy (OA) and number of parameters.