光学遥感图像目标检测的深度学习算法研究进展  

Progress of research on deep learning algorithms for object detection in optical remote sensing images

作　　者：徐丹青 吴一全[1] XU Danqing;WU Yiquan(College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学电子信息工程学院,南京211106

出　　处：《遥感学报》2024年第12期3045-3073,共29页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:61573183)。

摘　　要：光学遥感图像目标检测在军事和民用方面都有很广泛的应用前景。本文综述了基于深度学习的光学遥感图像目标检测算法研究进展。首先,详细分析了光学遥感图像中的目标特点;其次,简要回顾了光学遥感图像目标检测算法的发展历程;再次,阐明了基于深度学习的光学遥感图像目标检测流程;最后,以深度学习为出发点,概述了经典的目标检测框架,并根据光学遥感图像目标的特点,分别针对尺度多样性、方向多样性、形状多样性、小尺寸、特征相似性、背景复杂性、分布密集性、弱特征的光学遥感图像目标检测问题,对各种改进算法进行了系统性的总结,此外,归纳了针对光学遥感图像目标检测的非全监督学习算法,介绍了常用的开源光学遥感图像数据集和目标检测的性能评估指标,指出了现阶段在光学遥感图像目标检测领域仍然面临的主要挑战和下一步的发展方向。Among all applications of optical remote sensing images,object detection has always been given more attention by researchers.Object detection has a wide application prospect in military and civilian fields.This study reviews the progress of research on object detection algorithms in optical remote sensing images on the basis of deep learning.The characteristics of remote sensing objects are different from those of conventional objects.First,remote sensing equipment has a long imaging distance,so it can cover a large range.The images may have objects with large scale and shape changes.Second,in remote sensing images,the background tends to occupy a large area.As a result,some objects are often submerged in the complex background,and detectors cannot distinguish these objects effectively.Last,in remote sensing images,the objects do not only have a small size and changeable direction.Sometimes,remote sensing objects are densely distributed,posing challenges to the detection of optical remote sensing objects.This study introduces the development process of optical remote sensing object detection algorithms from template matching,prior knowledge,and machine learning to deep learning.Then,the process of optical remote sensing object detection based on deep learning,including data preprocessing,feature extraction,detection,and postprocessing,is introduced in detail.Classical deep learning-based object detection algorithms,including the one-stage algorithms represented by YOLO and SSD and the two-stage algorithm represented by Faster RCNN,are summarized.Afterward,in accordance with the characteristics of optical remote sensing image objects,various improved algorithms for addressing the optical remote sensing image object detection problems of scale diversity,direction diversity,shape diversity,small size,feature similarity,background complexity,distribution density,and weak features are systematically summarized.Non-strong supervised learning-based optical remote sensing image object detection methods and other advanced

关 键 词：光学遥感图像 目标检测 深度学习 目标特点 目标检测流程 目标检测框架 非全监督学习 数 据集 性能评估指标 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程] P2[天文地球—测绘科学与技术]