Metaformer-like Convolutional Neural Networks and Learnable Decision Fusion for SAR Ship Classification

With the increasing number of the ocean ships, the demand for synthetic aperture radar (SAR) image ship classification has been much increased. With the development of deep learning, many neural network-based ship classification methods have been presented. However, these networks show unsatisfactory performance on low-quality SAR ship datasets. In this paper, we propose a SAR ship classification method based on dual Metaformer-like networks and learnable decision fusion, which we call LDF-D-MLCNNs. First, we design a Metaformer-like convolutional block to improve learning performance. Secondly, we implement two networks with different kernel sizes and propose the learnable decision fusion module to obtain the final prediction. Kernels of different sizes exhibit diverse extraction capabilities. Experimental results show that the accuracy of the proposed method outperforms many existing SAR ship classification networks.