光学遥感图像目标检测算法综述

聂光涛; 黄华

doi:10.16383/j.aas.c200596

光学遥感图像目标检测算法综述

doi: 10.16383/j.aas.c200596 cstr: 32138.14.j.aas.c200596

聂光涛^1,,
黄华^2,

1.
北京理工大学计算机科学与技术学院北京 100081
2.
北京师范大学人工智能学院北京 100875

基金项目: 国家自然科学基金(61936011)资助

详细信息

作者简介:
聂光涛：北京理工大学计算机科学与技术学院博士研究生. 2015年获得西安工业大学电子信息工程学院学士学位. 2017年获得北京理工大学自动化学院硕士学位. 主要研究方向为计算机视觉, 目标检测和机器学习. E-mail: nieguangtao@bit.edu.cn

黄华：北京师范大学人工智能学院教授. 分别于1996年和2006年获得西安交通大学学士学位和博士学位. 主要研究方向为图像视频处理, 计算摄像学和计算机图形学. 本文通信作者. E-mail: huahuang@bnu.edu.cn

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出版历程
- 收稿日期: 2020-07-27
- 录用日期: 2020-12-01
- 网络出版日期: 2021-03-21
- 刊出日期: 2021-08-20

A Survey of Object Detection in Optical Remote Sensing Images

NIE Guang-Tao^1
,,
HUANG Hua^2
,

1.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081
2.
School of Artificial Intelligence, Beijing Normal University, Beijing 100875

Funds: Supported by National Natural Science Foundation of China (61936011)

More Information

Author Bio:
NIE Guang-Tao　Ph. D. candidate at the School of Computer Science and Technology, Beijing Institute of Technology. He received the his bachelor degree from the School of Electronics and Information Engineering, Xi＇ an Technological University in 2015, and the master degree from the School of Automation, Beijing Institute of Technology in 2017. His research interest covers computer vision, object detection, and machine learning

HUANG Hua　Professor at the School of Artificial Intelligence, Beijing Normal University. He received his bachelor and Ph. D. degrees from Xi＇ an Jiaotong University, in 1996 and 2006, respectively. His research interest covers image and video processing, computational photography, and computer graphics. Corresponding author of this paper

摘要

摘要:
目标检测技术是光学遥感图像理解的基础问题, 具有重要的应用价值. 本文对遥感图像目标检测算法发展进行了梳理和分析. 首先阐述了遥感图像目标检测的特点和挑战; 之后系统总结了典型的检测方法, 包括早期的基于手工设计特征的算法和现阶段基于深度学习的方法, 对于深度学习方法首先介绍了典型的目标检测模型, 进而针对遥感图像本身的难点详细梳理了优化改进方案; 接着介绍了常用的检测数据集, 并对现有方法的性能进行比较; 最后对现阶段问题进行总结并对未来发展趋势进行展望.
- 光学遥感 /
- 目标检测 /
- 手工设计特征 /
- 深度学习 /
- 数据集
Abstract:
Object detection in optical aerial images is a fundamental problem in the field of remote sensing and shows great importance in the application. The performance of the early hand-craft-feature algorithm is limited, while deep learning is the primary method for object detection at present. However, due to the characteristics of the remote sensing image itself, it is difficult for the existing detection algorithms to perform well on these images. In this paper, we first describe the characteristics and challenges of the object detection task in aerial images. We then summarize the typical detection methods, including early hand-craft feature extraction methods and current deep learning methods, especially for the deep learning algorithm enhancement upon the characteristics of the aerial images. Then the commonly used detection datasets are introduced, and the performances of the existing methods are compared. Finally, we summarize the current deficiencies and analyze the trends of future studies.
- Remote sensing /
- object detection /
- hand-craft features /
- deep learning /
- dataset

HTML全文

图 1 遥感图像目标检测的特点与挑战

Fig. 1 Characteristics and challenges of object detection in remote sensing images

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图 2 选择性搜索方法流程

Fig. 2 The process of selective search method

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图 3 水平框检测与旋转框检测对比

Fig. 3 Comparison of horizontal detection results and rotated detection results

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图 4 旋转框参数表示方案

Fig. 4 Parameter representation of rotated boxes

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图 5 边界突变问题示意说明

Fig. 5 Illustration of boundary mutation

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表 1 水平框检测算法性能对比

Table 1 Performance comparison of horizontal box detection algorithms

算法	主干	改进模块								mAP
算法	主干	超大覆盖	方向多样	尺度过小	密集分布	形状差异	尺度变化	外观模糊	复杂背景	mAP
NWPU VHR-10数据集
RICNN^[25]	AlexNet		√							73.10
R-P-Faster-RCNN^[134]	VGG-16									76.50
Def.R-FCN^[87]	Res-101					√				79.10
Def.Faster-RCNN^[88]	Res-50					√	√			84.40
RICADet^[111]	ZF		√					√		87.12
RDAS512^[72]	VGG-16			√			√			89.50
Multi-Scale CNN^[98]	VGG-16						√			89.60
CAD-Net^[108]	Res-101						√	√		91.50
SCRDet^[80]	Res-101			√	√		√		√	91.75
DOTA数据集
FR-H^[53]	Res-101	√								60.46
SBL^[135]	Res-50	√								64.77
FMSSD^[73]	VGG-16	√		√			√			72.43
ICN^[84]	Res-101	√					√			72.50
IoU-Adaptive^[110]	Res-101	√					√			72.72
EFR^[106]	VGG-16	√					√			73.49
SCRDet^[80]	Res-101	√		√	√		√		√	75.35
FADet^[89]	Res-101	√				√	√		√	75.38
MFIAR-Net t^[117]	Res-152	√					√		√	76.07
Mask OBB^[105]	ResX-101	√					√		√	76.98
A²RMNet^[90]	Res-101	√				√	√			78.45
OWSR^[109]	Res-101	√					√		√	78.79
Parallel Cascade R-CNN^[101]	ResX-101	√		√			√			78.96
DM-FPN^[99]	Res-101	√		√			√			79.27
SCRDet++^[82]	Res-101	√		√	√		√		√	79.35

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表 2 旋转框检测算法性能对比

Table 2 Performance comparison of rotated box detection algorithms

算法	主干	改进模块								mAP
算法	主干	超大覆盖	尺度过小	密集分布	形状差异	尺度变化	外观模糊	复杂背景	边界问题	mAP
FR-O^[119]	Res-101	√								52.93
IENet^[91]	Res-101	√			√	√			√	57.14
TOSO^[96]	Res-101	√			√				√	57.52
R-DFPN^[83]	Res-101	√				√				57.94
R²CNN^[121]	Res-101	√								60.67
RRPN^[120]	Res-101	√								61.01
Axis Learning^[95]	Res-101	√			√	√				65.98
ICN^[84]	Res-101	√				√				68.20
RADet^[107]	ResX-101	√	√			√		√	√	69.09
RoI-Transformer^[86]	Res-101	√				√				69.56
P-RSDet^[92]	Res-101	√			√					69.82
CAD-Net^[108]	Res-101	√				√	√			69.90
O²-DNet^[93]	HG-104	√			√					71.04
AOOD^[103]	Res-101	√	√			√				71.18
Cascade-FF^[116]	Res-152	√	√			√		√		71.80
SCRDet^[80]	Res-101	√	√	√		√		√	√	72.61
SARD^[124]	Res-101	√				√			√	72.95
GLS-Net^[118]	Res-101	√				√		√		72.96
DRN^[97]	HG-104	√			√					73.23
FADet^[89]	Res-101	√			√	√		√		73.28
MFIAR-Net^[117]	Res-152	√				√		√		73.49
R³Det^[81]	Res-152	√		√		√				73.74
RSDet^[126]	Res-152	√				√			√	74.10
Gliding Vertex^[123]	Res-101	√				√			√	75.02
Mask OBB^[105]	ResX-101	√				√		√	√	75.33
FFA^[104]	Res-101	√				√				75.70
APE^[94]	ResX-101	√			√				√	75.75
CSL^[125]	Res-152	√				√			√	76.17
OWSR^[109]	Res-101	√				√		√		76.36
SCRDet++^[82]	Res-101	√	√	√		√		√	√	76.81

下载: 导出CSV

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