基于深度学习的遥感图像目标检测算法综述

Zhe Zheng, Lin Lei, Hao Sun, Gangyao Kuang
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引用次数: 12

摘要

目标检测是遥感图像分析的重要组成部分。随着对地观测技术和卷积神经网络的发展,基于深度学习的遥感图像目标检测技术得到了越来越多的关注和研究。目前,已有许多优秀的目标检测算法被提出并应用于遥感领域。本文对遥感图像的目标检测算法进行了系统的总结,主要内容包括传统的遥感图像目标检测方法和基于深度学习的遥感图像目标检测方法,重点总结了基于深度学习的遥感图像目标检测算法及其发展历程,然后介绍了目标检测性能评价规则和常用的数据集。最后,对未来的发展趋势进行了分析和展望。希望本文的总结和分析能够为今后遥感领域目标检测技术的研究提供一些参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Remote Sensing Image Object Detection Algorithms Based on Deep Learning
Object detection is an important part of remote sensing image analysis. With the development of the earth observation technology and convolutional neural network, remote sensing image object detection technology based on deep learning has received more and more attention and research. At present, many excellent object detection algorithms have been proposed and applied in the field of remote sensing. In this paper, the object detection algorithms of remote sensing image is systematically summarized, the main contents include the traditional remote sensing image object detection method and the method based on deep learning, emphasis on summarize the remote sensing image object detection algorithm based on deep learning and its development course, then we introduced the rule of performance evaluation of object detection and datasets that commonly used. Finally, the future development trend is analyzed and prospected. It is hoped that this summary and analysis can provide some reference for future research on object detection technology in remote sensing field.
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