天基光学遥感动目标智能检测技术综述

肖超; 安玮; 李朝旭; 李博扬; 应昕怡; 林再平

doi:10.11834/jrs.20243277

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专辑

天基光学遥感动目标智能检测技术综述
Intelligent detection for moving targets in space-borne optical remote sensing： A review
2024年28卷第7期页码：1681-1692
收稿：2023-07-10，

纸质出版：2024-07-07
DOI： 10.11834/jrs.20243277
稿件说明：

移动端阅览

肖超，安玮，李朝旭，李博扬，应昕怡，林再平.2024.天基光学遥感动目标智能检测技术综述.遥感学报，28（7）： 1681-1692 DOI： 10.11834/jrs.20243277.

Xiao C，An W，Li Z X，Li B Y，Ying X Y，Lin Z P. 2024. Intelligent detection for moving targets in space-borne optical remote sensing： A review. National Remote Sensing Bulletin， 28（7）：1681-1692 DOI： 10.11834/jrs.20243277.

摘要

天基光学遥感动目标检测旨在对遥感卫星视频中具有连续运动特性的目标进行定位和分类，比如遥感视频卫星中的运动车辆、舰船和飞机。随着遥感视频卫星技术和深度学习技术的快速发展，基于模型驱动的传统遥感动目标检测方法正朝着基于数据驱动的深度学习方法进行演变，以完成高可靠、高时效、高性能的天基光学遥感图像动目标检测。本文介绍了光学遥感视频卫星的发展现状，并对基于模型驱动和基于数据驱动的光学遥感动目标检测方法进行了总结，梳理和分析了光学遥感动目标检测技术的发展历程。最后，在此基础上对光学遥感动目标检测的未来发展趋势进行了展望。

Abstract

Moving object detection in spaceborne optical remote sensing images (particularly for satellite videos) is a critical technique for interpreting remote sensing data. It has numerous applications

including surveillance

environmental monitoring

and military intelligence. Moving object detection in satellite videos aims to locate and classify moving targets accurately

such as moving vehicles

ships

trains

and airplanes. The advancements in spaceborne optical remote sensing satellite technology and the emergence of deep learning techniques allow the traditional model-driven methods for moving object detection to evolve toward data-driven deep learning methods and achieve high reliability

efficiency

and performance. This study introduces the current status of optical remote sensing satellite systems and summarizes model-driven and data-driven approaches for optical remote sensing moving target detection. First

the current state of video satellites is presented. Spaceborne optical video satellites hold great potential for advancing our ability to detect and monitor dynamic phenomena across various domains

thereby forking the data foundation for moving object detection. Second

the development of model-driven methods for moving object detection is analyzed

including frame differencing-based

optical flow-based

and background subtraction-based methods. However

these methods rely heavily on handcrafted features and prior knowledge to identify moving objects. Thus

they often struggle with complex backgrounds

varying lighting conditions

and occlusions. By contrast

with their powerful feature learning capabilities

data-driven deep learning-based methods have brought significant progress in moving object detection in satellite videos. Third

we present the development of data-driven deep learning-based methods. We also summarize the supervised and unsupervised deep learning-based methods for moving object detection in satellite videos

and their trends are discussed. In deep learning-based methods

extracting spatiotemporal information is crucial for efficient and effective moving object detection in satellite videos. Furthermore

the methods of alleviating annotation costs and addressing massive data are underdeveloped and need further exploration. Then

we introduce the datasets

evaluation criteria

and state-of-the-art experimental results. We evaluate nine representative model-driven and data-driven methods (supervised and unsupervised) for moving object detection in satellite videos on the VISO car dataset. We conclude from the results that deep learning methods with superior performance can adapt effectively to diverse environmental conditions and target characteristics. The unsupervised deep learning methods are underdeveloped and need considerable focus. Moreover

the combination of model-driven and data-driven methods has shown great potential for moving object detection

which leads to a new development trend for the community. Lastly

on the basis of the summary above and the discussion

we conclude the future trends of moving object detection in satellite videos. On the one hand

the development of satellite constellations can open up new possibilities for collaborative and distributive multisatellite and multisensor systems. On the other hand

the potential to merge feature representation

detection

and tracking into an end-to-end network with multitask learning needs further exploration. Weakly supervised and unsupervised methods are worth further development and research to alleviate the burden of annotation cost. The ability to tackle massive data and the construction of an end-to-end detection and recognition network for high-resolution satellite videos are also worth exploring.

关键词

Keywords

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