基于深度学习的SAR弱小目标检测研究进展
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金联合基金(U20B2068); 国家自然科学基金(62306005, 62006002, 62376004); 安徽省自然科学基金(2208085QF192)


Research Progress of SAR Weak Object Detection Based on Deep Learning
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    随着合成孔径雷达(SAR)技术的不断进步, 大范围观测和高分辨率成像使得SAR图像中包含了大量特征微弱的小尺寸目标, 通常涵盖飞机、车辆、油罐、船舶等高价值民用目标和关键军事目标, 这类目标尺寸较小、特征微弱、稠密相连、形态多变, 对它们进行精确的检测是当前SAR图像解译的难题. 随着深度学习技术的发展, 研究者们针对SAR弱小目标的成像特性和检测挑战, 通过对深度学习网络的精细调整和优化, 成功地推动了本领域的进步. 本文将全面回顾基于深度学习的SAR图像弱小目标检测, 以数据集和方法为研究对象, 深入分析SAR弱小目标检测任务所面临的主要挑战, 总结最新检测方法的特点和应用场景, 并汇总整理了公开数据集与常用性能评估指标. 最后, 总结本任务的应用现状, 并对未来的发展趋势进行展望.

    Abstract:

    Advancements in synthetic aperture radar (SAR) technology have enabled large-scale observations and high-resolution imaging. Consequently, SAR images now contain numerous small-sized objects with weak features, including aircraft, vehicles, tanks, and ships, which are of high value in civilian and key military assets. However, accurately detecting these objects poses a significant challenge due to their small size, dense connectivity, and variable morphology. Deep learning technology has ushered in a new era of progress in SAR object detection. Researchers have made substantial strides by fine-tuning and optimizing deep learning networks to address the imaging characteristics and detection challenges associated with weak SAR objects. This study provides a comprehensive review of deep learning-based methodologies for weak object detection in SAR images. The primary focus is on datasets and methods, providing a thorough analysis of the principal challenges encountered in SAR weak object detection. This study also summarizes the characteristics and application scenarios of recent detection methods, as well as collates and organizes publicly available datasets and common performance evaluation metrics. In conclusion, this study provides an overview of the current application status of SAR weak object detection and offers insights into future development trends.

    参考文献
    相似文献
    引证文献
引用本文

赵志成,蒋攀,王福田,肖云,李成龙,汤进.基于深度学习的SAR弱小目标检测研究进展.计算机系统应用,2024,33(6):1-15

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-12-21
  • 最后修改日期:2024-01-23
  • 录用日期:
  • 在线发布日期: 2024-05-09
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号