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遥感技术与应用  2022, Vol. 37 Issue (2): 290-305    DOI: 10.11873/j.issn.1004-0323.2022.2.0290
综述     
基于深度学习的遥感图像目标检测技术研究进展
付涵1,2(),范湘涛1,严珍珍1(),杜小平1
1.中国科学院空天信息创新研究院数字地球重点实验室,北京 100094
2.中国科学院大学,北京 100094
Progress of Object Detection in Remote Sensing Images based on Deep Learning
Han Fu1,2(),Xiangtao Fan1,Zhenzhen Yan1(),Xiaoping Du1
1.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
2.University of Chinese Academy of Sciences,Beijing 100094,China
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摘要:

目标检测是遥感图像信息提取领域中的研究热点之一,具有广泛的应用前景。近些年来,深度学习在计算机视觉领域的发展为海量遥感图像信息提取提供了强大的技术支撑,使得遥感图像目标检测的精确度和效率均得到了很大提升。然而,由于遥感图像目标具有多尺度、多种旋转角度、场景复杂等特点,在高质量标记样本有限的情况下,深度学习在遥感图像目标检测应用中仍面临巨大挑战。从尺度不变性、旋转不变性、复杂背景干扰、样本量少和多波段数据检测5个角度出发,总结了近几年基于深度学习的遥感图像目标检测方法。此外,对典型遥感图像目标的检测难点和方法进行分析和总结,并对公开的遥感图像目标检测数据集进行概述。最后阐述了遥感图像目标检测研究的未来趋势。

关键词: 遥感图像目标检测深度学习卷积神经网络    
Abstract:

Object detection has always been a hot topic in the field of remote sensing images information extraction, and has a wide range of application prospect in many fields. The development of deep learning in the field of computer vision provides a strong technical support for the extraction of massive remote sensing images, and greatly improves the accuracy and efficiency of object detection in remote sensing images. However, objects in remote sensing images have the characteristics of multiple scales, multiple rotation angles and complex scenes, deep learning technique still faces great difficulties in the application of remote sensing images object detection with limited high-quality labeled samples. According to five aspects of scale invariance, rotation invariance, complex background interference, limited training samples and detection of multi-band data, the existing algorithms of object detection based on deep learning in the field of remote sensing images in recent years are introduced and summarized. In addition, the difficulties and methods of detecting typical objects in remote sensing images are analyzed and summarized, and the common datasets of remote sensing images object detection including optical images and SAR images are also given general introduction. Finally, the future trends of object detection in remote sensing images are analyzed.

Key words: Remote sensing images    Object detection    Deep learning    Convolutional Neural Network
收稿日期: 2020-11-10 出版日期: 2022-06-17
ZTFLH:  TP75  
基金资助: 国家重点研发计划(2018YFC1504201D);中国科学院战略先导科技专项(A类)(XDA 19080101);国家自然科学基金面上项目(41974108)
通讯作者: 严珍珍     E-mail: fuhan@radi.ac.cn;yanzz@radi.ac.cn
作者简介: 付涵(1994-),女,河北易县人,博士研究生,主要从事深度学习在遥感图像处理中的应用研究。E?mail:fuhan@radi.ac.cn
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引用本文:

付涵,范湘涛,严珍珍,杜小平. 基于深度学习的遥感图像目标检测技术研究进展[J]. 遥感技术与应用, 2022, 37(2): 290-305.

Han Fu,Xiangtao Fan,Zhenzhen Yan,Xiaoping Du. Progress of Object Detection in Remote Sensing Images based on Deep Learning. Remote Sensing Technology and Application, 2022, 37(2): 290-305.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0290        http://www.rsta.ac.cn/CN/Y2022/V37/I2/290

图1  自下而上特征融合
图2  自上而下特征融合
文献方法特点
Zhu等[20],2019自下而上融合受文献[27]启发,提取不同卷积层(conv3,conv4,conv5)候选区域对应的特征向量,经过兴趣特征池化层(ROI Pooling)、L2归一化,以促进多尺度特征融合
Dong等[19],2020自下而上融合将进行膨胀扩张的卷积神经网络分支(conv4)和顶层卷积层的反卷积输出(conv5)融合,以有效利用高层语义信息
Yang等[21],2018自上而下融合在ResNet网络基础上构建了5层特征金字塔
Zhu等[22],2019自上而下融合在密集网络DenseNet上构建了3层特征金字塔网络以获取小目标特征
Zhuang等[23],2019自上而下融合在Darknet-53网络基础上构建了3层特征金字塔
Zhang等[24],2019自上而下融合构建了一种多尺度区域建议网络和多尺度目标检测网络,同时,提出了多尺度训练策略(使用基于斑块的多尺度训练数据以及使用多尺寸的图像进行训练)并进行消融实验
Qiu等[25],2019自上而下融合在特征金字塔基础上加入一个门融合(gate fusion)模块,使得模型可以选择性地融合CNN不同尺度特征
Cheng等[26],2020自上而下以及跨尺度融合在构建FPN的基础上,提出了一种跨尺度特征融合策略,对每个特征尺度进行特征融合和特征增强
表1  实现尺度不变性的目标检测方法
图3  加入旋转角度的包围框
图4  自适应旋转ROI
文献方法特点
Cheng等[29],2016旋转不变特征层在AlexNet网络中加入一个旋转不变层,通过正则化约束项优化目标函数,使旋转前后的训练样本具有相似特征,从而实现旋转不变性
Cheng等[30],2019旋转不变特征层和Fisher判别层在通用的目标检测模型基础上,加入旋转不变正则化和Fisher判别正则化以解决目标旋转、类内多样性和类间相似度等问题
Tang等[31],2017导向矩形框对包围框的偏移量加入一个角度参数,得到,通过默认包围框以及偏移量得到具有方向的矩形框
Zhang等[32],2018导向矩形框在包围框参数中加入角度参数来预设锚点,得到五元组,并采用R2oI池化层进行最大池化操作
Yang等[21],2018导向矩形框构建任意旋转矩形,并提出自适应的ROI Align结构自动过滤因旋转框造成的ROI噪声
Dong等[33],2020导向矩形框构建任意旋转矩形,并设计旋转的ROI pooling进行最大池化
Yu等[34],2020导向矩形框构建旋转矩形,设计导向锚点子网络,并构建旋转ROI pooling进行最大池化
表2  实现旋转不变性的目标检测方法
图5  上下文信息提取(红色框为目标信息范围,蓝色框为上下文信息范围)
文献方法特点
Li等[37],2018提取局部上下文特征构建了一种基于混合限制玻尔兹曼机的双通道框架,融合了局部和上下文特征(分别提取原始建议框1倍和1.5倍特征)
Ren等[38],2018提取上下文信息将提取的上下文区域和RPN产生的建议区域分别经过ROI pooling层,之后连接两部分向量用于预测
Zhang等[40],2019提取全局上下文信息及构建注意力模块设计了一种全局上下文敏感网络,GCNet,来学习全局场景语义;提出了空间感知模块,以关注图像上信息更丰富的区域
Gong等[39],2020提取局部上下文信息对每个ROI挖掘一个自适应的上下文ROI提取上下文特征(定义3倍ROI区域为有效的上下文区域)
Chen等[41],2018注意力模块提出了一种两层注意力机制模块,结合了局部和全局信息
Pang等[43],2019注意力模块基于特征金字塔池化构建了一种全局注意力模块
李红艳等[44],2019注意力模块使用结合空间和通道的注意力机制模块CBAM
表3  解决复杂目标信息的目标检测方法
文献方法特点
Zhou等[45],2016;Zhong等[46],2018;Dong等[47],2019迁移学习将在大规模自然图像数据集上的训练模型用于遥感图像目标检测,并进行训练微调
Zhu等[18],2020数据增广用生成对抗网络GAN产生新的数据集来训练模型
Han等[48],2015弱监督学习提出了一种基于贝叶斯原理的弱监督学习框架,对弱标注的样本进行训练,通过迭代更新正样本集标签
Zhang等[49],2015弱监督学习通过基于显著性自适应分割和负挖掘得到初始训练样本,然后通过迭代训练逐步细化训练样本
Zhou等[45],2016弱监督学习提出了一种负样本自举的弱监督目标检测框架,基于显著性的自适应分割方法收集初始正样本,通过生成最可能的正样本和视觉上与正样本最相关的负样本来初始化训练样本,然后使用这些样本迭代训练检测器
Zhang等[50],2016弱监督学习首先初始化训练样本,其次通过挖掘的负样本、标记样本以及辅助数据进行迭代训练,更新训练样本
表4  解决样本量少的目标检测方法
文献方法特点
Li等[51],2017CNN从训练样本中选择像素对来训练CNN,用训练好的CNN衡量测试像素和周围环境的相似度,并对这些相似度值进行平均作为最终测试输出
Cheng等[52],2018CNN使用CNN来提取高光谱图像深层特征,同时提出了一种基于度量学习的框架来学习判别频谱空间特征,最终在SVM中嵌入一个度量学习正则化项以实现高光谱图像分类
Zhou等[16],2019堆叠自动编码器提出了一个高光谱图像分类框架CDSAE,利用局部Fisher判别正则化和多样性正则化来训练模型
Xie等[17],2020光谱正则化无监督网络基于AE和VAE构建了光谱正则化无监督网络,证明了其在目标检测上表现更好
表5  高光谱图像目标检测方法
文献方法特点
Ding等[53],2016CNN结合卷积神经网络和三种数据增广方法,实现对SAR图像目标自动识别
Liu等[54],2017CNN提出了基于海陆分割的CNN框架,结合CNN、显著性计算和角点特性实现SAR图像船舶检测
Wang等[55],2018SSD结合迁移学习和SSD模型提高SAR图像检测精度,降低误报率
Zhao等[56],2018耦合CNN以耦合CNN作为特征提取网络,提出了基于脉冲余弦变换的视觉注意力方法,从频域角度进行船舶识别
Fan等[57],2019分割模型基于全卷积网络提出了一种针对极化SAR图像的船舶分割模型,有效减少海杂波和SAR图像模糊影响
Lin等[58],2019注意力机制结合SENet和FasterR-CNN减少船舶误检、漏检情况
Chang等[59],2019YOLOv2基于YOLOv2提出了一种层数更少的新框架,更好地减少计算量和模型检测时间
Wei等[60],2020高分辨率金字塔网络提出了一种高分辨率特征金字塔模型用于提取SAR图像中高层和低层特征,并采用缓和非极大抑制方法(Soft-NMS)提高密集船舶目标检测性能
Chen等[61],2020双注意力机制提出了一种多尺度双注意目标检测网络用于机场提取
表6  SAR图像目标检测方法
图6  各类别精确度分布
数据集名称类 别图像总数实例个数包围框年份
NWPU VHR-10108003 775垂直包围框2014
UCAS-AOD22 42014 596垂直包围框2015
RSOD49766 950垂直包围框2015
HRSC201611 0612 976旋转包围框2016
LEVIR322 00011 000垂直包围框2018
DOTA152 806188 282旋转包围框2018
DIOR2023 463192 472垂直包围框2020
SSDD11 1602 456垂直包围框2017
SAR-Ship-Dataset143 81943 819垂直包围框2019
HRSID15 60416 951垂直包围框2020
表7  公开的遥感图像目标检测数据集
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