Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs18010053

Authors: Shanhong Guo, Hairui Zhu, Ji Zhu, Weixing Sheng, Jiachen Tan

Institution: Nanjing University of Science and Technology

Language: English

Topic: Advanced SAR Imaging Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Natural Science Foundation of China, National Key Laboratory of Transient Physics, Fundamental Research Funds for the Central Universities