小样本SAR图像分类方法综述  

Few-shot SAR image classification:a survey

作　　者：王梓祺 李阳 张睿 王家宝 李允臣 陈瑶 Wang Ziqi;Li Yang;Zhang Rui;Wang Jiabao;Li Yunchen;Chen Yao(Command and Control Engineering College,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]陆军工程大学指挥控制工程学院,南京210007

出　　处：《中国图象图形学报》2024年第7期1902-1920,共19页Journal of Image and Graphics

基　　金：江苏省自然科学基金项目(BK20200581)。

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。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 learningbased 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 develop 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 fewshot 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.Firs

关 键 词：小样本学习 SAR图像分类 迁移学习 元学习 度量学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]