小样本SAR图像分类方法综述

王梓祺; 李阳; 张睿; 王家宝; 李允臣; 陈瑶

doi:10.11834/jig.230359

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专辑

小样本SAR图像分类方法综述
Few-shot SAR image classification： a survey
2024年29卷第7期页码：1902-1920
收稿日期：2023-06-20，

修回日期：2023-11-03，

纸质出版日期：2024-07-16
DOI： 10.11834/jig.230359
稿件说明：

移动端阅览

王梓祺，李阳，张睿，王家宝，李允臣，陈瑶. 2024. 小样本SAR图像分类方法综述. 中国图象图形学报， 29(07):1902-1920 DOI： 10.11834/jig.230359.

Wang Ziqi， Li Yang， Zhang Rui， Wang Jiabao， Li Yunchen， Chen Yao. 2024. Few-shot SAR image classification： a survey. Journal of Image and Graphics， 29(07):1902-1920 DOI： 10.11834/jig.230359.

摘要

合成孔径雷达（synthetic aperture radar，SAR）图像分类作为SAR图像应用的重要底层任务受到了广泛关注与研究。SAR图像分类是处理和分析遥感图像的重要手段，在环境监测、目标侦察和地质勘探等任务中发挥着关键作用，但是目前基于深度学习的SAR图像分类任务存在小样本问题。本文针对小样本SAR图像分类方法进行全面的论述和分析。1）介绍了SAR图像分类任务的重要性和早期的SAR图像分类方法，并阐述了小样本SAR图像分类任务的必要性。2）介绍了小样本SAR图像分类任务的定义、常用的数据集、评价指标和应用。3）整理了各类方法的贡献点和使用的数据集，将已有的小样本SAR图像分类方法分为基于迁移学习的方法、基于元学习的方法、基于度量学习的方法和综合性方法4类。根据分类总结了4类方法存在的缺陷，为后续工作提供了一定的参考。在统一的框架内测试了16种可见光数据集方法迁移到SAR图像数据集上的分类性能，并从分类精度和运行时间两个方面综合评估了小样本学习模型迁移效果。该项工作利用SAR图像分类通用数据集MSTAR（moving and stationary target acquisition and recognition）完成，极大地补充了小样本SAR图像分类任务的测评基准。4）对小样本SAR图像分类方法的发展趋势进行了展望，提出了未来可能的一些严峻挑战。

Abstract

Few-shot synthetic aperture radar （SAR） image classification aims to use a small number of training samples to classify new SAR images and facilitate subsequent vision tasks further. In recent years， it has received widespread attention in the field of image processing， especially playing a crucial role in tasks such as environmental monitoring， target reconnaissance， and geological exploration. Moreover， the growth of deep learning has been promoting deep learning-based few-shot SAR image classification. In particular， the improvement of few-shot learning algorithm， such as the attention mechanism， transfer learning， and meta learning， has led to a qualitative leap in few-shot SAR image classification performance. However， a comprehensive review and analysis of state-of-the-art deep learning-based few-shot SAR image classification algorithms for different complex scenes need to be conducted. Thus， we dev

elop a systematic and critical review to explore the developments of few-shot SAR image classification in recent years. First， a comprehensive and systematic introduction of the few-shot SAR image classification field is presented from three aspects： 1） overview of early SAR image classification methods， 2） the existing dataset， and 3） the prevailing evaluation metrics. Then， the existing few-shot SAR image classification methods are categorized into four types： transfer learning， meta learning， metric learning， and comprehensive methods. The main contributions and the datasets used for each method are summarized. Therefore， we test the classification accuracy and runtime of 16 classic few-shot visible light image classification methods on the moving and stationary target acquisition and recognition （MSTAR） dataset. In this way， the evaluation benchmark for few-shot SAR image classification methods is supplemented for future research reference. Finally， the summary and challenges in the few-shot SAR image classification community are highlighted. In particular， some prospects are recommended further in the field of few-shot SAR image classification. First， starting from the classification criteria， SAR image classification methods can be divided into four categories based on the feature information used， whether manual sample labeling is required， technical methods， and processing objects. These traditional SAR image classification methods lay the foundation for subsequent few-shot SAR image classification methods. We briefly introduce the popular public datasets and prevailing evaluation metrics. The existing datasets for few-shot SAR image classification include MSTAR， OpenSARShip， COSMO-SkyMed， FuSAR-Ship， OpenSARUrban， and SAR-ACD. Among them， MSTAR is the most commonly used standard few-shot SAR image classification dataset. The evaluation indicators for method performance in few-shot SAR image classification tasks mainly include classification accuracy， precision， and recall. Precision and recall represent

two different indicators， which is why intuitively reflecting the performance of the model is difficult. Therefore， the harmonic mean of these two indicators has become a direct indicator for judging the performance of the model. In addition， few-shot learning commonly uses top 1 and top 5 as evaluation indicators. Second， few-shot SAR image classification methods based on deep learning can be divided into three categories： transfer learning， meta learning， and metric learning. Transfer learning methods quickly adapt to the new class image classification by using the association between similar tasks to assist the model after completing the pre-training on a large number of base class data. This type of method can effectively overcome the problem of insufficient training samples in the field of SAR images. Meta learning methods aim to enable models to learn by training a meta learner to evaluate the dataset learning process and gain learning experience. Then， the model utilizes the acquired learning experience to complete relevant classification tasks on the target dataset. Metric learning methods are an end-to-end training approach that utilizes data from each

-shot category to learn a feature embedding space. In this feature embedding space， the model can more effectively measure the similarity between samples. This type of method relatively reduces the difficulty of training feature extractors， making the structure of the model more flexible and able to quickly adapt to the task of identifying new classes. As a result of the different imaging principles between SAR images and visible light images， some comprehensive methods guided by physical knowledge and domain knowledge have also been used in SAR image classification tasks and achieved great results. Therefore， in addition to the above three classification methods， some methods that combine deep learning and SAR image characteristics have been applied to solve the problem of few-shot SAR image classification. We summarize the limitations o

f different few-shot SAR image classification algorithms and provide some recommendations for further research. Third， we tested the classification performance of 16 visible light dataset methods migrating to SAR image datasets within a unified framework and comprehensively evaluated the transfer effect of few-shot learning models from two aspects： classification accuracy and runtime. This work can effectively supplement the evaluation benchmark for few-shot SAR image classification tasks. The experiment found that the few-shot learning method based on metric learning achieved good performance in the field of SAR image classification without comprehensive methods. Finally， the review summarizes the future development trends and challenges of few-shot SAR image classification based on a summary of existing methods.

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