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Survey of Research Progress on Target Detection and Discrimination of Single-channel SAR Images for Complex Scenes
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摘要: SAR作为一种主动式微波成像传感器，以其全天时、全天候、作用距离远等独特的技术优势，成为当前对地观测的主要手段之一，在军事和民用领域发挥着十分重要的作用。随着SAR遥感技术的发展，高分辨率、高质量的SAR图像不断产生，仅依靠人工手段对感兴趣的目标进行检测、识别费时费力，因此亟需发展SAR自动目标识别(ATR)技术。典型的SAR ATR系统主要包括检测、鉴别、分类/识别3个阶段，其中，检测和鉴别阶段是整个SAR ATR系统的基础，是国内外雷达界一直开展的SAR应用基础研究之一。针对单通道SAR图像，简单场景下目标检测与鉴别已经取得了不错的结果；而在复杂场景下，杂波散射强度相对高、杂波背景非均匀和目标散射强度相对弱、分布密集等情况，使得SAR目标检测和鉴别依然是一个难点。该文对近十年左右复杂场景下单通道SAR目标检测及鉴别方法的研究进展进行了归纳总结，并分析了各类方法的特点及存在的问题，展望了未来复杂场景下单通道SAR目标检测与鉴别方法的发展趋势。Abstract: As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.
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图 5 GF-3港口舰船目标数据集中图像的例子
Figure 5. Example of image in GF-3 ship target dataset in the port area
表 1 不同SAR目标检测方法比较
Table 1. Comparison of different SAR target detection methods
SAR目标检测方法 优点 缺点 基于恒虚警率 方法简单，简单场景下可以取得良好性能。 实际中很难选择出合适的杂波统计模型且处理非均匀强杂波背景下的目标检测问题时易产生较多虚警和漏警。 基于视觉注意模型 速度较快，复杂场景下可以通过引入先验信息一定程度上抑制强杂波增强目标，有效提升信杂比。 先验信息的引入需要针对具体的问题具体分析，对于不同的检测任务可能需要重新设计算法。 基于复图像 利用SAR目标和杂波的散射特性以及成像机理进行目标检测，从物理机理上更能反映人造目标和自然杂波的区别。 需要原始复数SAR图像数据，目前对人造杂波干扰和感兴趣人造目标的区分能力还有待验证。 
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表 2 SAR目标鉴别方法总结
Table 2. Summary of SAR target discrimination methods
SAR目标鉴别方法 优点 缺点 基于全监督学习 算法简单、精度较高。 需要大量人工标记的训练数据。 基于半监督、弱监督学习 需要人工标记的信息较少，减轻人类的工作负担。 算法较为复杂，且性能与全监督方法还存在一定的差距。
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表 3 SAR目标检测、鉴别2级流程与基于深度学习的检测、鉴别一体化方法比较
Table 3. Comparison of SAR target detection and discrimination based on two stage process with detection and discrimination integration method based on deep learning
SAR目标检测、鉴别方法 优点 缺点 2级流程 结合SAR图像目标、杂波物理特性提取特征或设计分类器。一些传统2级流程方法计算量较小，在相同的平台条件下，运算效率更高，对资源占用也更少，更易于工程实用。 特征和分类器独立设计，二者可能失配，影响最终性能。 基于深度学习的一体化 不用手工设计特征和分类器，检测精度较高。 需要大量有标记的数据支撑，而SAR图像可以得到的有标记数据较少。网络参数较多，对计算、存储资源要求高，通常需要GPU平台。
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