面向卫星遥感图像场景重建的神经辐射场方法综述

周鑫; 王洋; 孙显; 林道玉; 刘俊义; 付琨

doi:10.11999/JEIT240202

面向卫星遥感图像场景重建的神经辐射场方法综述

doi: 10.11999/JEIT240202

周鑫^{1, 2, 3, 4},
王洋^{1, 2},
孙显^{1, 2, 3},
林道玉^{1, 2},
刘俊义^{1, 2, ,},
付琨^{1, 2, 3}

1.
中国科学院空天信息创新研究院北京 100190
2.
中国科学院网络信息体系技术重点实验室北京 100190
3.
中国科学院大学北京 100190
4.
中国科学院大学电子电气与通信工程学院北京 100190

详细信息

作者简介:
周鑫：男，博士生，研究方向为基于隐式神经表示的重建与渲染

王洋：女，副研究员，研究方向为时空大数据可视分析

孙显：男，研究员，研究方向为计算机视觉与遥感图像理解，地理空间大数据解译

林道玉：男，助理研究员，研究方向为图像生成与场景渲染

刘俊义：男，研究员，研究方向为地理空间大数据挖掘，多源信息智能融合

付琨：男，研究员，研究方向为计算机视觉与遥感图像理解，地理空间信息挖掘与可视化

通讯作者:
刘俊义　liujy004735@aircas.ac.cn

中图分类号: TN911.6
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-25
- 修回日期: 2024-05-13
- 网络出版日期: 2024-05-17
- 刊出日期: 2024-05-30

A Review of Neural Radiance Field Approaches for Scene Reconstruction of Satellite Remote Sensing Imagery

ZHOU Xin^{1, 2, 3, 4},
WANG Yang^{1, 2},
SUN Xian^{1, 2, 3},
LIN Daoyu^{1, 2},
LIU Junyi^{1, 2
, ,},
FU Kun^{1, 2, 3}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100190, China
4.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China

摘要

摘要: 随着高分辨率卫星遥感图像成为认知地理空间不可或缺的重要手段，卫星遥感图像在城市建图、生态监测和导航等领域发挥着日益重要的作用，利用卫星遥感图像进行地球表面大规模3维重建成为了计算机视觉和摄影测量领域的研究热点。神经辐射场(NeRF)利用可微渲染学习场景的隐式表示，在复杂场景新视图合成任务中实现了逼真的视觉效果，并在3维场景重建和渲染领域获得了极大的关注。近期的研究主要集中在利用神经辐射场技术，从卫星遥感图像中提取场景表示及其重建。面向卫星遥感图像的神经辐射场方法主要集中在光线空间优化、场景表示优化以及模型高效训练3方面。该文全面归纳了神经辐射场技术在卫星遥感应用中的最新进展。首先介绍神经辐射场技术的基本概念及相关数据集。然后提出一个面向卫星遥感图像的神经辐射场方法分类框架，用于系统性地回顾和整理该技术在卫星遥感领域的研究进展。接着详述了神经辐射场技术在实际卫星遥感场景应用中的相关成果。最后，基于当前研究所面临的问题和挑战进行分析和讨论，同时对未来的发展趋势和研究方向进行了展望。
- 神经辐射场 /
- 卫星遥感图像 /
- 神经渲染 /
- 隐式表示
Abstract: High-resolution satellite remote sensing images have been recognized as an indispensable means for understanding geographical spaces, and their role in areas such as urban mapping, ecological monitoring, and navigation, has become increasingly important. The use of satellite remote sensing images for large-scale 3D reconstruction of the Earth’s surface is currently a subject of active research in the fields of computer vision and photogrammetry. Neural Radiance Fields (NeRF), which utilizes differentiable rendering to learn implicit representations of scenes, has achieved the most realistic visual effects in novel view synthesis tasks of complex scenes and has attracted significant attention in the field of 3D scene reconstruction and rendering. Recent research has been primarily focused on using neural radiance field technology to extract scene representation and reconstruction from satellite remote sensing images. Ray space optimization, scene representation optimization, and efficient model training are mainly focused on by the neural radiance field methods for satellite remote sensing images. The latest progress in the application of neural radiance field technology in satellite remote sensing is comprehensively summarized in this paper. First, the basic concepts of neural radiance field technology and related datasets are introduced. Then a classification framework of neural radiance field methods for satellite remote sensing images is proposed to systematically review and organize the research progress of this technology in the field of satellite remote sensing. The relevant results of the application of neural radiance field technology in actual satellite remote sensing scenarios are detailed. Finally, analysis and discussion are conducted based on the problems and challenges faced by current research, and future development trends and research directions are prospected.
- Neural Radiance Field(NeRF) /
- Satellite remote sensing imagery /
- Neural rendering /
- Implicit representation

HTML全文

图 1 NeRF体积渲染和训练过程^[1]

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图 2 神经辐射场方法流程

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图 3 S-NeRF渲染模型^[16]

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图 4 SpS-NeRF立体匹配重建结果^[2]

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图 5 Sat-Mesh网格模型重建结果^[18]

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图 6 Season-NeRF季节特征风格转换^[26]

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表 1 神经辐射场常用数据集

数据集名称	类别	场景数量	分辨率/ 像素	每场景图像数量
Synthetic NeRF	物体	8	800×800	400
LLFF	前向场景	8	1008×756	20～62
Mip-NeRF 360	室内、室外场景	9	1237×822	100～330
Tanks and Temples	室外场景	4	1920×1080	283

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表 2 DFC2019数据集详细信息

	1	2	3	4
区域编号	004	068	214	260
输入图像	9	17	21	15
高度范围	[–24, 1]	[–27, 30]	[–29, 73]	[–30, 13]
纬度	30.357	30.348	30.316	30.311
经度	–81.706	–81.663	–81.663	–81.663

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表 3 光线空间优化方法总结

光线空间	优点	缺点	代表方法
球坐标表示	易于理解和计算	精度略低	S-NeRF(2021年)
ECEF坐标表示	适合精确位置计算	计算变换复杂且不直观	Sat-NeRF(2022年)
UTM坐标表示	易于理解和应用	需要额外的高度信息	EO-NeRF(2023年)
RPC近似模型表示	精度较高，灵活性高	近似过程存在误差	Sat-Mesh(2023年)

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