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Research Progress and Prospects of SAR Image Target Detection Based on Multi-source Information Cross-domain Learning
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摘要: 深度学习虽已成为合成孔径雷达(SAR)图像目标检测领域主流方法，但其性能高度依赖于大规模标注数据集。在测试不同分布的SAR数据时，模型的检测性能会出现一定程度下降，难以直接迁移应用，且人工标注成本高昂。针对此瓶颈，学术界开始探索基于多源信息的跨域学习策略，通过整合不同来源(传感器)的光学遥感图像或异源SAR图像等先验信息，辅助检测模型实现跨域知识迁移。该文聚焦深度学习框架下的跨域学习技术，系统梳理该领域最新研究进展，分析梳理该领域的核心问题，从方法论层面分析现有技术的核心优势与适用场景，并基于技术演进规律提出未来发展方向，旨在为提升SAR图像目标检测的泛化能力提供理论支撑与方法论参考。Abstract: Deep learning is primarily used for target detection in synthetic aperture radar (SAR) images; however, its performance heavily relies on large-scale labeled datasets. The detection performance of deep learning models degrades when applied to SAR data with varying distributions, hindering their real-world applicability. In addition, manual labeling of SAR data is costly. Hence, cross-domain learning strategies based on multisource information are being explored to address these challenges. These strategies can assist detection models in realizing cross-domain knowledge migration by integrating prior information from optical remote sensing images or heterogeneous SAR images acquired from different sensors. This paper focuses on cross-domain learning technologies within the deep learning framework. In addition, it provides a systematic overview of the latest research progress in this field and analyzes the core issues, advantages, and applicable scenarios of existing technologies from a methodological perspective. It outlines future research directions based on the law of technological evolution, aiming to offer theoretical support and methodological references to enhance the generalizability of target detection in SAR images.
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图 1 某港口的高分辨率SAR图像与对应的光学遥感图像对比
Figure 1. Comparison between high-resolution SAR images and corresponding optical remote sensing images of a certain port
图 2 不同雷达参数对检测模型(YOLOv5n)的影响
Figure 2. The influence of different radar parameters on the detection model (YOLOv5n)
图 3 近年发表跨域SAR图像目标检测关键论文时间表
Figure 3. Timeline of key papers on cross-domain SAR image target detection published in recent years
图 5 域适应和域泛化的训练测试过程示意图
Figure 5. Schematic diagram of the training and testing process for domain adaptation and domain generalization
图 6 不同成像条件下的SAR舰船数据集t-SNE分布
Figure 6. The t-SNE distribution map of SAR ship dataset under different imaging
图 7 光学和SAR舰船数据集t-SNE分布
Figure 7. The t-SNE distribution map of optical and SAR ship datasets
图 8 域特有性与不变性特征学习流程示意图
Figure 8. Schematic diagram of domain specific and invariant feature learning process
图 13 统计特征动态调整流程示意图
Figure 13. Schematic diagram of dynamic adjustment of statistical features process
图 14 视觉基础大模型微调流程示意图
Figure 14. Schematic diagram of the fine-tuning process for large visual foundation models
表 1 基于深度学习的非跨域和跨域学习方法对比
Table 1. Comparison of non/cross-domain learning methods based on deep learning
方法 适用场景 优势 局限性 面临挑战 非跨域学习 指训练与测试数据分布
基本一致的情形数据准备和训练过程简单、
模型容易优化需大量标注样本、模型训练成本高、
泛化性差、易过拟合SAR标注数据稀缺、模型泛化
能力不足、计算资源需求大跨域学习 指训练与测试数据分布存在
差异的情形缓解数据稀缺问题、可利用
多源信息、泛化能力强域差异建模复杂、知识迁移效率低、
易引入噪声、方法设计难度高SAR图像数据获取难、域分布
差异大、跨域知识迁移失效
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表 2 7类跨域SAR图像目标检测方法对比
Table 2. Comparison of seven cross-domain SAR image target detection methods
方法 核心思路 优势 局限性 适用场景 域特有性与不变性特征学习 将特征空间分解为域特有与
不变特征能有效建模域间差异
与共性需要有效的特征分解算法，
增加计算复杂度适用于域间成像差异但仍处于同一模态空间的场景；[SAR→SAR] 伪标签生成 基于自训练生成伪标签，通过
伪标签增强训练数据在标签稀缺的情况下
可以提升性能伪标签质量难以控制，可能
引入噪声，影响模型效果适用于标签稀缺或标注成本高的
任务；［两者］图像风格转换 将源域与目标域图像风格转化为一致的风格，减少风格差异影响 能从输入层面对齐
模态差异可能无法捕捉到目标域的
真实特征，风格转换质量
可能不稳定适用于源与目标域视觉风格差异较大的场景；[光学→SAR] 特征对齐 对源域和目标域特征进行对齐，使其在共享空间中表现相似 与检测网络结合紧密，
可端到端训练。对齐的准确性影响模型性能，且过度对齐可能损失判别信息 适用于特征差异较大或目标域数据变化较大的任务；［两者］ 先验知识约束 利用已有的先验知识(如物理特
性或标签信息)引导模型学习能充分利用SAR成像
物理特性需要有效的先验知识支持，
依赖领域专业知识适用于典型SAR目标(如舰船、
车辆)检测，以及具有明确成像
规律的场景；［两者］统计特征动态调整 通过调整统计参数生成
不同的源域样本方法简单，易于与
现有模型结合仅能对齐低阶统计特征，
增加训练成本适用于模拟复杂未知环境或需要处理多个域数据的任务［SAR→SAR］ 视觉基础大模型微调 利用预训练大模型学习源域
知识加速目标域学习简单直接，性能提升明显 易过拟合目标域，训练成本高 适用于大规模源域数据预训练任务；[光学→SAR] 注：“适用场景”中使用[SAR→SAR]、[光学→SAR]或[两者]说明每类方法更适用于哪种情况.
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表 3 2021年以来在相同跨域数据集上实验的论文(2篇以上)统计
Table 3. Statistics of two or more papers experimented on the same cross-domain dataset in recent years
任务 数据 类别 文献 SAR→SAR SSDD$\leftrightarrow $Gaofen-3 舰船 [17,43,45,58,71] miniSAR$\leftrightarrow $FARADSAR 车辆 [16,52,53,56] 光学→SAR LEVIR→SSDD 舰船 [67,72,75] HRRSD→SSDD [44,46,68,70,72] LEVIR→SAR-Ship-Dataset [67,72,75] DIOR→SSDD [55,57,60,65−67,70,80] HRSC2016→SSDD [47,50,75,84] HRSC2016→HRSID [47,50,69,84] Toronto→MiniSAR 车辆 [12,48,78,79] Toronto→FARADSAR [12,78,83]
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表 4 3种车辆检测数据集的成像参数
Table 4. Imaging parameters of three vehicle detection datasets
成像参数 Toronto miniSAR FARADSAR 传感器 光学 机载SAR 星载SAR 波段 - Ku Ka、X 带宽(GHz) - 3 3、5 时间(年) 2016 2005 2015 场景 高尔夫球场、直升机场、棒球场等 密集城市建筑 晴天密集的城市建筑 位置 克特兰空军基地 新墨西哥大学 加拿大多伦多市 俯仰角度(°) - 26～29 26～34 入射角度(°) - 61～64 56～61 分辨率(m) 0.15 0.10 0.10
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表 5 典型方法在Toronto到miniSAR和FARADSAR上跨域检测性能对比(%)
Table 5. Comparison of cross-domain detection performance of typical methods on Toronto to miniSAR and FARADSAR (%)
方法 Toronto→miniSAR Toronto→FARADSAR P R F1-score P R F1-score Gaussian-CFAR 35.87 80.48 49.62 28.13 46.71 35.12 Faster R-CNN[31] 70.97 90.36 79.39 74.82 92.19 82.60 文献[48](无监督) 77.60 78.05 77.82 - - - 文献[79](少样本) 80.49 73.47 76.82 - - - 文献[12] (无监督) 80.16 87.80 83.81 78.82 71.43 74.95 文献[78](半监督) 81.69 91.87 85.92 81.84 92.91 86.81 文献[79] (少样本) 84.56 93.50 88.76 - - -
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表 6 2种星载SAR舰船检测数据集的成像参数
Table 6. Imaging parameters of spaceborne SAR for two types of ship detection datasets
成像参数 Gaofen-3数据 SSDD 卫星 Gaofen-3 Sentinel-1 Radarsat-2 TerraSAR-X 波段 C C C X 带宽(MHz) 240 100 100 150 轨道高度(km) 755 693 798 514 入射角度(°) 10～60 10～60 20～45 20～55 俯仰角扫描角度(°) ±20 ±20 ±11 ±25 成像范围(km) 10～650 20～400 20～50 5～100 分辨率(m) 0.5～100 5～20 1～100 1～16
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表 7 典型方法在Gaofen-3和SSDD上跨域检测性能对比(%)
Table 7. Comparison of cross-domain detection performance of typical methods on Gaofen-3 and SSDD (%)
方法 SSDD→Gaofen-3 Gaofen-3→SSDD P R mAP P R mAP Faster-RCNN[31] 57.0 71.0 57.9 62.5 77.8 67.0 文献[71](无监督) 69.8 79.9 68.4 74.6 82.9 78.1 文献[17](少样本) 72.3 74.6 76.5 75.4 77.0 78.6 文献[43] (无监督) 73.7 81.9 74.4 78.4 86.3 81.5 文献[58](自监督) 74.1 82.8 75.8 78.9 87.0 82.6 文献[45] (无监督) 74.8 83.3 77.0 79.8 86.3 83.6
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