光学遥感图像目标检测的深度学习算法研究进展

徐丹青; 吴一全

doi:10.11834/jrs.20243166

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光学遥感图像目标检测的深度学习算法研究进展
Research Progress of Deep Learning Algorithms for Object Detection in Optical Remote Sensing Images
2024年页码：1-30
网络出版日期： 2024-03-07 ，
DOI： 10.11834/jrs.20243166

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徐丹青，吴一全.XXXX.光学遥感图像目标检测的深度学习算法研究进展.遥感学报，XX（XX）： 1-30

XU Danqing，WU Yiquan. XXXX. Research Progress of Deep Learning Algorithms for Object Detection in Optical Remote Sensing Images. National Remote Sensing Bulletin， XX（XX）：1-30
徐丹青，吴一全.XXXX.光学遥感图像目标检测的深度学习算法研究进展.遥感学报，XX（XX）： 1-30 DOI： 10.11834/jrs.20243166.

XU Danqing，WU Yiquan. XXXX. Research Progress of Deep Learning Algorithms for Object Detection in Optical Remote Sensing Images. National Remote Sensing Bulletin， XX（XX）：1-30 DOI： 10.11834/jrs.20243166.

摘要

在光学遥感图像的各种应用中，目标检测历来受到研究人员的重视，并且在军事和民用方面都有很广泛的应用前景。本文综述了基于深度学习的光学遥感图像目标检测算法研究进展。首先，详细分析了光学遥感图像中的目标特点；然后，简要回顾了光学遥感图像目标检测算法的发展历程；其次，阐明了基于深度学习的光学遥感图像目标检测流程；接着，以深度学习为出发点，概述了经典的目标检测框架，并根据光学遥感图像目标的特点，分别针对尺度多样性、方向多样性、形状多样性、小尺寸、特征相似性、背景复杂性、分布密集性、弱特征的光学遥感图像目标检测问题，对各种改进算法进行了系统性的总结，此外，归纳了针对光学遥感图像目标检测的非全监督学习算法；随后，介绍了常用的开源光学遥感图像数据集和目标检测的性能评估指标；最后，指出了现阶段在光学遥感图像目标检测领域仍然面临的主要挑战和下一步的发展方向。

Abstract

In all kinds of applications of optical remote sensing image

object detection has always been paid more attention by researchers. It has a very wide application prospect in military and civilian aspects. This paper reviews the research progress of object detection algorithms in optical remote sensing images based on deep learning. The characteristics of remote sensing objects are different from those of conventional objects. Firstly

remote sensing equipment has a long imaging distance

so it can cover a large range. There may be objects with large scale and shape changes in the images. Secondly

in remote sensing images

the background tends to occupy a large area. This leads to the object often submerged in the complex background

which makes it difficult for the detectors to distinguish the objects effectively. Thirdly

in remote sensing images

the objects are not only reflected in the aspects of small size and changeable direction. Sometimes the remote sensing objects are often densely distributed

which brings some challenges to the detection of optical remote sensing objects.Firstly

this paper introduces the development process of optical remote sensing object detection algorithms from template matching

prior knowledge

and machine learning to deep learning. Then

the process of optical remote sensing object detection based on deep learning is introduced in detail

including data preprocessing

feature extraction

detection and post-processing. Thirdly

the classical deep learning-based object detection algorithms

including the one-stage algorithms represented by YOLO and SSD as well as the two-stage algorithm represented by Faster RCNN are summarized. Fourthly

according to the characteristics of optical remote sensing image object s

various improved algorithms are systematically summarized for optical remote sensing image object detection problems of scale diversity

direction diversity

shape diversity

small size

feature similarity

background complexity

distribution density and weak features. Fifthly

non-strong supervised learning based optical remote sensing image object detection methods and other advanced algorithms

such as transformer based algorithms

transfer learning based algorithms

knowledge graph based algorithms and prior knowledge based algorithms are summarized. In addition

the open source optical remote sensing image datasets and the performance of object detection evaluation indexes are introduced.At present

mAP of advanced algorithms in NWPU-VHR10 dataset can reach more than 90%. For DOTA dataset

mAP of each advanced algorithm has decreased significantly

and the advanced algorithms proposed in recent years still continue to improve the performance of this dataset. Multi-scale fusion with feature pyramid network has become the mainstream method of advanced algorithms

which can detect multi-scale objects effectively.In order to solve the problems of optical remote sensing image object detection mentioned above

many improved algorithms have been proposed and good detection results have been achieved. However

there is still a lack of research on object detection of large scale remote sensing images and similar objects detection between classes. At last

this paper proposes future development directions

such as improved deep learning networks

lightweight networks

weakly supervised learning

small object detection

and improved rotary detection mechanism.

关键词

光学遥感图像目标检测深度学习目标特点目标检测流程目标检测框架非全监督学习数据集性能评估指标

Keywords

optical remote sensing imageobject detectionobject characteristicsobject detection processobject detection frameworkdeep learningdatasetincomplete supervised learningevaluation index

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