基于深度学习的光学遥感图像目标检测研究进展

廖育荣; 王海宁; 林存宝; 李阳; 方宇强; 倪淑燕

doi:10.11959/j.issn.1000-436x.2022071

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基于深度学习的光学遥感图像目标检测研究进展

综述 | 更新时间：2024-06-05

- 基于深度学习的光学遥感图像目标检测研究进展
- Research progress of deep learning-based object detection of optical remote sensing image
- 通信学报 2022年43卷第5期页码：190-203
- 作者机构：
  
  1. 航天工程大学电子与光学工程系，北京 101416
  2. 航天工程大学研究生院，北京 101416
- 作者简介：
  
  [ "廖育荣（1972- ），男，四川德阳人，博士，航天工程大学研究员，主要研究方向为空间智能信息处理、航天测控通信、卫星信号处理技术等" ]
  [ "王海宁（1998- ），男，河南南阳人，航天工程大学硕士生，主要研究方向为遥感图像处理、深度学习、目标检测" ]
  [ "林存宝（1987- ），男，吉林延边人，博士，航天工程大学助理研究员，主要研究方向为空间光学载荷、空间智能信息处理" ]
  [ "李阳（1993- ），女，辽宁大连人，航天工程大学博士生，主要研究方向为遥感图像处理、目标检测" ]
  [ "方宇强（1984- ），男，四川乐山人，博士，航天工程大学副教授，主要研究方向为计算机视觉、机器学习、深度学习" ]
  [ "倪淑燕（1981- ），女，河北清河人，博士，航天工程大学副教授，主要研究方向为数字图像处理、空间智能信息处理等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61805283);国家自然科学基金资助项目(61805284);国家自然科学基金资助项目(61906213)
- DOI：10.11959/j.issn.1000-436x.2022071
  中图分类号： TP391.41
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-25
- 稿件说明：
移动端阅览
廖育荣, 王海宁, 林存宝, 等. 基于深度学习的光学遥感图像目标检测研究进展[J]. 通信学报, 2022,43(5):190-203.

Yurong LIAO, Haining WANG, Cunbao LIN, et al. Research progress of deep learning-based object detection of optical remote sensing image[J]. Journal on communications, 2022, 43(5): 190-203.
廖育荣, 王海宁, 林存宝, 等. 基于深度学习的光学遥感图像目标检测研究进展[J]. 通信学报, 2022,43(5):190-203. DOI： 10.11959/j.issn.1000-436x.2022071.

Yurong LIAO, Haining WANG, Cunbao LIN, et al. Research progress of deep learning-based object detection of optical remote sensing image[J]. Journal on communications, 2022, 43(5): 190-203. DOI： 10.11959/j.issn.1000-436x.2022071.

摘要

目标检测是光学遥感图像解译中的核心问题，在情报侦察、目标监视、灾害救援等领域均具有广泛应用。首先，结合深度学习光学遥感图像目标检测算法研究进展，对基于候选区域和回归分析的两类遥感目标检测算法进行了综述。其次，针对旋转目标、小目标、多尺度、密集目标四类常见特定任务场景目标检测算法改进进行了总结。再次，结合常用遥感图像数据集对不同算法性能进行了对比分析。最后，对未来遥感图像目标检测研究值得关注的问题进行了展望，为后续相关研究提供思路。

Abstract

Object detection is the core issue in the interpretation of optical remote sensing images

and it is widely used in fields such as intelligence reconnaissance

target monitoring

and disaster rescue.Firstly

combined with the research progress of deep learning optical remote sensing image object detection algorithms

the two types of algorithms based on candidate regions and regression analysis were reviewed.Secondly

the improvement of object detection algorithms for four types of common task-specific scenes were summarized

including rotating objects

small objects

multi-scales

and dense objects.Then

combined with commonly used remote sensing image data sets

the performance of different algorithms was compared and analyzed.Finally

the issues worthy of attention in remote sensing image object detection in the future were prospected

and ideas for follow-up related research were provided.

关键词

Keywords

references

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