Recognition Method of Ship Targets for SAR Based on M3Net

WANG Hao-tian; JI Zhen-yuan; HUA Qing-long; GUO Zhao-xin; ZHANG Yun

doi:10.12263/DZXB.20250356

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Recognition Method of Ship Targets for SAR Based on M3Net

PAPERS | 更新时间：2026-02-05

- Recognition Method of Ship Targets for SAR Based on M3Net
- ACTA ELECTRONICA SINICA Vol. 53, Issue 10, Pages: 3759-3772(2025)
- 作者机构：
  
  哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62401183);China Postdoctoral Science Foundation(2024M754185;GZB20240967);Heilongjiang Postdoctoral Fund(LBH-Z24138)
- DOI：10.12263/DZXB.20250356
  CLC： TP751;
- Received：05 May 2025，
  
  Revised：2025-10-12，
  
  Published：25 October 2025
- 稿件说明：
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王浩添, 冀振元, 化青龙, 等. 基于多分支多信息多深度复值特征融合网络的SAR舰船目标识别方法[J]. 电子学报, 2025, 53(10): 3759-3772.

WANG Hao-tian, JI Zhen-yuan, HUA Qing-long, et al. Recognition Method of Ship Targets for SAR Based on M3Net[J]. Acta Electronica Sinica, 2025, 53(10): 3759-3772.
王浩添, 冀振元, 化青龙, 等. 基于多分支多信息多深度复值特征融合网络的SAR舰船目标识别方法[J]. 电子学报, 2025, 53(10): 3759-3772. DOI：10.12263/DZXB.20250356

WANG Hao-tian, JI Zhen-yuan, HUA Qing-long, et al. Recognition Method of Ship Targets for SAR Based on M3Net[J]. Acta Electronica Sinica, 2025, 53(10): 3759-3772. DOI：10.12263/DZXB.20250356

摘要

针对合成孔径雷达（Synthetic Aperture Radar，SAR）图像中舰船目标识别任务中存在的类内差异显著与类间相似性高的难题，本文提出一种基于多分支多信息多深度复值特征融合网络（Multi-Branch， Multi-Information， Multi-Depth feature fusion complex-valued Network，M3Net）的舰船目标识别方法.传统方法多依赖人工设计的幅度特征，未能充分利用SAR原始数据中固有的复数特性，忽略了相位信息及其与幅度的耦合关系，导致对舰船精细结构的表征能力不足，识别精度与模型泛化能力受限.本文通过深入分析舰船目标的非圆性和复信号峰度特征，发现两者能够有效表征舰船目标区别于海面背景的散射特性，揭示了复数域统计量对舰船散射特性的表征优势.在此基础上，本文设计了深度复值特征提取模块（Complex Feature Extraction Module，CFEM），通过复卷积运算提取幅相耦合特征，创新性地引入实虚交融激活函数（Cross-fusion of Real and Imaginary Activation，CRIA），通过双激活函数的交叉耦合机制实现非线性特征交互，增强了对复数特征的表征能力.进一步构建多分支多信息多深度融合网络M3Net，通过主干复数域卷积神经网络（Complex-Valued Convolutional Neural Network，CV-CNN）、预训练CFEM分支和实值特征分支的协同处理，结合复数域注意力机制实现异构特征的动态加权融合，自适应突出判别性强的特征通道.在重构OpenSARship数据集上的实验结果表明，所提方法较传统CV-CNN提升5.89%，极差值降低至6.82%，显著改善了类别均衡性.

Abstract

To address the challenges of significant intra-class variations and high inter-class similarities in ship target recognition within synthetic aperture radar (SAR) images

this paper proposes a novel recognition method based on a multi-branch

multi-information

multi-depth feature fusion complex-valued network (M3Net). Traditional methods predominantly rely on manually designed amplitude features

failing to fully exploit the inherent complex-valued nature of raw SAR data and neglecting the crucial phase information and its coupling relationship with amplitude. This limitation results in insufficient characterization of ships’ fine structures and ultimately restricts recognition accuracy and model generalization capability. Through in-depth analysis of the noncircularity and complex signal kurtosis characteristics of ship targets

this study reveals that these features can effectively characterize the scattering properties distinguishing ships from the sea background

highlighting the representational advantages of complex-domain statistics for ship scattering characteristics. Building on this foundation

a deep complex feature extraction module (CFEM) is designed. This module employs complex-valued convolutional operations to extract amplitude-phase coupled features and innovatively introduces a cross-fusion of real and imaginary activation (CRIA) function. The CRIA mechanism

utilizing a dual-activation function cross-coupling approach

achieves nonlinear feature interactions and enhances the representational capacity for complex-valued features. Furthermore

the multi-branch

multi-information

multi-depth fusion network M3Net is constructed. M3Net synergistically integrates a core complex-valued convolutional neural network (CV-CNN) backbone

a pre-trained CFEM branch

and a real-valued feature branch. By incorporating a complex-domain attention mechanism

M3Net achieves dynamic weighted fusion of these heterogeneous features

adaptively highlighting the most discriminative feature channels. Experimental results on the reconstructed OpenSARship dataset demonstrate the effectiveness of the proposed method. Compared to the traditional CV-CNN

our approach achieves a 5.89% improvement in overall accuracy and reduces the maximum accuracy deviation across classes to 6.82%

significantly enhancing category balance.

关键词

Keywords

references

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