Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

doi:10.3390/rs14102385

CIOMP OpenIR

	Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey
	Z. Li; Y. C. Wang; N. Zhang; Y. X. Zhang; Z. K. Zhao; D. D. Xu; G. L. Ben and Y. X. Gao
	2022
发表期刊	Remote Sensing
卷号	14 期号:10 页码:41
摘要	Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.
DOI	10.3390/rs14102385
URL	查看原文
收录类别	sci ; ei
语种	英语
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66509
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. Li,Y. C. Wang,N. Zhang,et al. Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey[J]. Remote Sensing,2022,14(10):41.
APA	Z. Li.,Y. C. Wang.,N. Zhang.,Y. X. Zhang.,Z. K. Zhao.,...&G. L. Ben and Y. X. Gao.(2022).Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey.Remote Sensing,14(10),41.
MLA	Z. Li,et al."Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey".Remote Sensing 14.10(2022):41.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Deep Learning-Based （5226KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载