{"title":"少发合成孔径雷达目标识别综述","authors":"Junjun Yin;Changxian Duan;Hongbo Wang;Jian Yang","doi":"10.1109/JSTARS.2024.3454266","DOIUrl":null,"url":null,"abstract":"Synthetic aperture radar (SAR) has the advantage of providing imaging capabilities throughout the day and under all-weather conditions, which makes it particularly important for Earth observation applications. Recently, the utilization of deep learning for SAR image recognition has become a crucial discipline in radar image interpretation since the deepened networks can generate the high-dimensional features and make the function fit accurately when with a large amount of training samples. However, for SAR images, the accurate annotation demands significant effort, expert knowledge, and is prone to errors due to the effect of noise. The lack of SAR-labeled data limits the application of deep neural networks, which usually need a large number of training samples. Consequently, the task of recognizing SAR targets in the scenario with a few training samples has emerged as a significant research interest and, accordingly, the few-shot target recognition technique was introduced and has shown great potential. This article provides a summary of recent advancements in few-shot SAR image target recognition. First, this article outlines the concept of few-shot learning and discusses the dataset specific to the SAR recognition field. Subsequently, it delves into a detailed categorization of methods for recognizing few-shot SAR targets, which include approaches based on the transfer learning, data augmentation, metalearning, and model-based strategies. Finally, it examines both qualitative and quantitative aspects of SAR automatic target recognition technology utilizing few-shot learning, highlights certain challenges and crucial issues that require great attention, and offers a perspective on future research opportunities.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664499","citationCount":"0","resultStr":"{\"title\":\"A Review on the Few-Shot SAR Target Recognition\",\"authors\":\"Junjun Yin;Changxian Duan;Hongbo Wang;Jian Yang\",\"doi\":\"10.1109/JSTARS.2024.3454266\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Synthetic aperture radar (SAR) has the advantage of providing imaging capabilities throughout the day and under all-weather conditions, which makes it particularly important for Earth observation applications. Recently, the utilization of deep learning for SAR image recognition has become a crucial discipline in radar image interpretation since the deepened networks can generate the high-dimensional features and make the function fit accurately when with a large amount of training samples. However, for SAR images, the accurate annotation demands significant effort, expert knowledge, and is prone to errors due to the effect of noise. The lack of SAR-labeled data limits the application of deep neural networks, which usually need a large number of training samples. Consequently, the task of recognizing SAR targets in the scenario with a few training samples has emerged as a significant research interest and, accordingly, the few-shot target recognition technique was introduced and has shown great potential. This article provides a summary of recent advancements in few-shot SAR image target recognition. First, this article outlines the concept of few-shot learning and discusses the dataset specific to the SAR recognition field. Subsequently, it delves into a detailed categorization of methods for recognizing few-shot SAR targets, which include approaches based on the transfer learning, data augmentation, metalearning, and model-based strategies. Finally, it examines both qualitative and quantitative aspects of SAR automatic target recognition technology utilizing few-shot learning, highlights certain challenges and crucial issues that require great attention, and offers a perspective on future research opportunities.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664499\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10664499/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10664499/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Synthetic aperture radar (SAR) has the advantage of providing imaging capabilities throughout the day and under all-weather conditions, which makes it particularly important for Earth observation applications. Recently, the utilization of deep learning for SAR image recognition has become a crucial discipline in radar image interpretation since the deepened networks can generate the high-dimensional features and make the function fit accurately when with a large amount of training samples. However, for SAR images, the accurate annotation demands significant effort, expert knowledge, and is prone to errors due to the effect of noise. The lack of SAR-labeled data limits the application of deep neural networks, which usually need a large number of training samples. Consequently, the task of recognizing SAR targets in the scenario with a few training samples has emerged as a significant research interest and, accordingly, the few-shot target recognition technique was introduced and has shown great potential. This article provides a summary of recent advancements in few-shot SAR image target recognition. First, this article outlines the concept of few-shot learning and discusses the dataset specific to the SAR recognition field. Subsequently, it delves into a detailed categorization of methods for recognizing few-shot SAR targets, which include approaches based on the transfer learning, data augmentation, metalearning, and model-based strategies. Finally, it examines both qualitative and quantitative aspects of SAR automatic target recognition technology utilizing few-shot learning, highlights certain challenges and crucial issues that require great attention, and offers a perspective on future research opportunities.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.