Cross-Modal SAR Target Detection via Progressive Knowledge Transfer

ZHAO Guo-wei; JIANG Jia-qing; DONG Gang-gang

doi:10.12263/DZXB.20250417

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Cross-Modal SAR Target Detection via Progressive Knowledge Transfer

PAPERS | 更新时间：2025-12-27

- Cross-Modal SAR Target Detection via Progressive Knowledge Transfer
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3147-3162(2025)
- 作者机构：
  
  西安电子科技大学雷达信号处理全国重点实验室，陕西西安 710071
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61971324;62525105)
- DOI：10.12263/DZXB.20250417
  CLC： TN95;
- Received：25 May 2025，
  
  Accepted：13 August 2025，
  
  Published：25 September 2025
- 稿件说明：
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赵国威, 蒋嘉庆, 董刚刚. 跨模态渐进式知识迁移SAR目标检测技术[J]. 电子学报, 2025, 53(09): 3147-3162.

ZHAO Guo-wei, JIANG Jia-qing, DONG Gang-gang. Cross-Modal SAR Target Detection via Progressive Knowledge Transfer[J]. Acta Electronica Sinica, 2025, 53(09): 3147-3162.
赵国威, 蒋嘉庆, 董刚刚. 跨模态渐进式知识迁移SAR目标检测技术[J]. 电子学报, 2025, 53(09): 3147-3162. DOI：10.12263/DZXB.20250417

ZHAO Guo-wei, JIANG Jia-qing, DONG Gang-gang. Cross-Modal SAR Target Detection via Progressive Knowledge Transfer[J]. Acta Electronica Sinica, 2025, 53(09): 3147-3162. DOI：10.12263/DZXB.20250417

摘要

随着光/SAR传感器的投入使用，如何挖掘异源数据信息、提高遥感图像解译与信息获取效率，是当前亟待解决的科学问题.对此，论文提出了一种基于光/SAR（Synthetic Aperture Radar）图像渐进式知识迁移的跨模态目标检测方法（Progressive Crossmodal Knowledge Transfer，PCKT），充分挖掘可见光图像丰富的纹理细节信息，改善SAR图像目标检测性能.第一，针对可见光遥感与SAR截然不同的成像机理导致模态差异大、信息融合难的问题，提出从可见光图像到SAR图像的跨域生成技术，利用可见光图像语义信息引导构建伪SAR中间域，实现光学辐射特征与SAR散射特性的语义融合；第二，针对可见光与SAR图像之间存在的语义鸿沟、跨模态学习效果差的问题，设计光/SAR多尺度特征对齐学习策略，实现可见光源域-伪SAR中间域语义特征、伪SAR中间域-SAR目标域散射特性分布的高效对齐，形成分阶段的跨模态知识迁移学习框架；第三，针对光/SAR模态离群样本对跨模态学习的不利影响，提出基于质量感知的动态权重分配机制，根据域分类器置信度衡量动态量化合成样本的可靠性，在训练过程中优先学习高置信度样本，从而抑制离群中间域图像产生的不利影响.最后，利用SpaceNet6、SSDD（SAR Ship Detection Dataset）、HRSID（High-Resolution SAR Images Dataset）等典型异源数据进行了大量跨模态学习实验验证，结果表明所提方法能充分挖掘可见光图像的重要信息，改善SAR图像目标检测效果，与源域无迁移方法相比，论文所提出方法在SpaceNet同源知识迁移结果提升平均检测精度为21.5个百分点，与次优基线方法相比平均检测精度提升为3.3个百分点，很好地验证了跨模态知识迁移在SAR目标检测任务中的可行性.

Abstract

The wide application of electro-optical sensors presented the urgent need of cross-modal learning between optical and SAR image. In this paper

a new cross-modal synthetic aperture radar (SAR) target detection method via progressive knowledge transfer was proposed. First

a new generative technique from the optical image to SAR image was presented. The immediate domain composed of the generated pseudo SAR images can be formed accordingly. The semantic discrepancies between SAR backscattering imaging mechanism and the passive optical radiation imagery can be bridged. The optical radiation features with SAR scattering characteristics can be fused effectively. Second

a dual-stage domain adaptation strategy composed of the multi-scale feature alignment skill was presented. The semantic components between the optical source domain and the intermediate domain can be aligned through the multi-scale feature learning trick initially. The scattering distribution alignment between the intermediate domain and the SAR target domain can be then achieved. Third

a quality-aware dynamic weighting strategy was presented to mitigate the impact of outlier samples in the intermediate domain. It was capable of adjusting the contributions of synthetic data based on confidence metrics dynamically. Finally

multiple rounds of experiments were pursued on SpaceNet6

SSDD (SAR Ship Detection Dataset)

and HRSID (High-Resolution SAR Images Dataset) datasets. The results proved the advantages of proposed method. The improvement of 21.5 percentage points was achieved compared to the source-only learning method. Likewise

the improvement of 3.3 percentage points was achieved in comparison to the state-of-the-art. These results confirm the viability of electro-optical-to-SAR knowledge transfer for enhancing cross-modal target detection.

关键词

Keywords

references

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