Side-Scan Sonar Image Classification with Zero-Shot and Style Transfer

Side-scan sonar (SSS) has become an important tool for ocean exploration due to its practicality and reliability. Existing approaches for SSS image classification mainly rely on deep convolutional neural networks (DCNNs). Although effective, DCNN-based methods fail to classify SSS images with specific target samples that are completely absent from the training data. In this work, a global context external-attention network (GCEANet) is proposed to produce pseudo-SSS images corresponding to the absent categories for zero-shot SSS image classification. First, the optical and acoustic images are taken as content and style image inputs to the network, respectively. Second, a global context (GC) block is proposed to extract the context semantics of the style features and fuse them with the content features. Finally, an external attention (EA) block is proposed to extract the potential correlation between content and style features to generate pseudo-SSS images with optical content. In addition, a contrastive loss based on image reconstruction is proposed to optimize the cross-modal feature space fusion. Extensive experiments demonstrate the effectiveness of our approach in efficiently generating realistic pseudo-SSS samples. The experimental results on real SSS sample datasets show that our method outperforms other state-of-the-art (SOTA) methods in the field of zero-shot SSS image classification. The code is available at https://github.com/baizhongyu/GCEANet.