摘要
航空遥感图像目标检测旨在定位和识别遥感图像中感兴趣的目标,是航空遥感图像智能解译的关键技术,在情报侦察、灾害救援和资源勘探等领域具有重要应用价值。然而由于航空遥感图像具有尺寸大、目标小且密集、目标呈任意角度分布、目标易被遮挡、目标类别不均衡以及背景复杂等诸多特点,航空遥感图像目标检测目前仍然是极具挑战的任务。基于深度卷积神经网络的航空遥感图像目标检测方法因具有精度高、处理速度快等优点,受到了越来越多的关注。为推进基于深度学习的航空遥感图像目标检测技术的发展,本文对当前主流遥感图像目标检测方法,特别是2020—2022年提出的检测方法,进行了系统梳理和总结。首先梳理了基于深度学习目标检测方法的研究发展演化过程,然后对基于卷积神经网络和基于Transformer目标检测方法中的代表性算法进行分析总结,再后针对不同遥感图象应用场景的改进方法思路进行归纳,分析了典型算法的思路和特点,介绍了现有的公开航空遥感图像目标检测数据集,给出了典型算法的实验比较结果,最后给出现阶段航空遥感图像目标检测研究中所存在的问题,并对未来研究及发展趋势进行了展望。
Given the successful development of aerospace technology,high-resolution remote-sensing images have been used in daily research.The earlier low-resolution images limit researchers’interpretation of image information.In comparison,today’s high-resolution remote sensing images contain rich geographic and entity detail features.They are also rich in spatial structure and semantic information.Thus,they can greatly promote the development of research in this field.Aerial remote sensing image object detection aims to provide the category and location of the target of interest in aerial remote sensing images and present evidence for further information interpretation reasoning.This technology is crucial for aerial remote sensing image interpretation and has important applications in intelligence reconnaissance,target surveillance,and disaster rescue.The early remote sensing image object detection task mainly relies on manual interpretation.The interpretation results are greatly affected by subjective factors,such as the experience and energy of the interpreters.Moreover,the timeliness is low.Various remote sensing image object detection methods based on machine learning technology have been proposed with the progress and development of machine learning technology.Traditional machine learning-based object detection techniques generally use manually designed models to extract feature information,such as feature spectrum,gray value,texture,and shape of remote sensing images,after generating sliding windows.Then,they feed the extracted feature information into classifiers,such as support vector machine(SVM)and adaptive boosting(AdaBoost),to achieve object detection in remote sensing images.These methods design the corresponding feature extraction models for specific targets with strong interpretability but weak feature expression capability,poor generalization,time-consuming computation,and low accuracy.These features make meeting the needs of accurate and efficient object detection tasks challenging in complex and vari
作者
石争浩
仵晨伟
李成建
尤珍臻
王泉
马城城
Shi Zhenghao;Wu Chenwei;Li Chengjian;You Zhenzhen;Wang Quan;Ma Chengcheng(School of Computer Science and Engineering,Xi’an University of Technology,Xi’an 710048,China;Key Laboratory of Aviation Science and Technology for Integrated Circuit and Microsystem Design,Xi’an Xiangteng Micro-Electronic Technology Co.,Ltd.,Xi’an 710068,China)
出处
《中国图象图形学报》
CSCD
北大核心
2023年第9期2616-2643,共28页
Journal of Image and Graphics
