A survey on cloud removal in optical remote sensing images: progress, challenges, and future works

doi:10.11947/j.AGCS.2025.20230137

Abstract

Abstract:

Optical remote sensing images (RSIs), which are widely used in various Earth observation tasks due to its rich geoinformation, are often significantly affected by varying degrees of cloud contamination, leading to a significant reduction in data quality and usability. Although extensive research has been conducted on cloud removal from optical RSIs, there is still a lack of systematic review and analysis in this field. To address this gap, this paper first employs bibliometric analysis to investigate the publication trends of relevant literature both domestically and internationally, revealing the long-term development dynamics of cloud removal research in RSIs. Subsequently, the paper then provides a comprehensive and systematic review of research on the removal of thin and thick clouds, thoroughly analyzing the core challenges, underlying assumptions, approaches, and fundamental principles of different cloud removal methods, while evaluating their strengths and weaknesses. Finally, this paper summarizes and discusses the common key challenges and future trends in current optical remote sensing cloud removal research. This paper not only offers crucial insights for readers to fully understand the research progress in optical remote sensing cloud removal over the past three decades but also serves as a valuable reference for grasping the development patterns and trends in this field.

Key words: optical remote sensing image, cloud removal, image restoration, missing information reconstruction

CLC Number:

P237

Xinchang ZHANG, Ji QI, Chao TAO, Siyang FU, Mingning GUO, Yongjian RUAN. A survey on cloud removal in optical remote sensing images: progress, challenges, and future works[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(4): 603-620.

Figures/Tables 4

Fig. 1

Fig. 2

Tab. 1

Overview of studies on thin cloud removal in optical remote sensing images and representative approaches"

大类	子类	代表性工作	说明
物理先验驱动	辐射传输模型（RTM）	传统RTM^[15-17]	对整个大气辐射传输过程建模以去除大气或云雾干扰
		物理视角简化RTM^[18-19]	对云雾干扰下的辐射传输过程进行建模
		统计视角简化RTM^[20-22]	用数据统计规律或经验公式近似替代传统RTM的部分物理建模过程
	暗目标减法（DOS）	全局DOS^[23-24]	对整个影像搜索暗目标并作为先验引导云雾去除
		局部DOS^[25-26]	对影像的不同局部区域分别提取暗目标
		暗通道先验法^[27-30]	从影像中提取像素的暗通道作为先验信息
	云雾优化变换（HOT）	传统HOT方法^[31-32]	手动选取晴空线来计算HOT图并引导云雾去除
		自动化HOT^[33-34]	自动选取影像中的代表性无云区域以定义晴空线
		掩膜HOT^[35]	将部分明亮地物进行掩膜以消除虚假响应
		迭代HOT^[36-37]	通过迭代挖掘额外参考影像的信息以提升HOT图准确性
		AHOT^[35]	通过非线性变换得到HOT响应图
		HOT&云轨迹^[37]	建模过程考虑了云雾干扰的乘性影响
大类	子类	代表性工作	说明
数据特征驱动	空间域特征滤波（SDFF）	中值滤波法^[38]	将薄云视为稀疏椒盐噪声，并用中值滤波去云
		均值滤波法^[39]	将薄云视为低频噪声，用均值滤波提取图像的低频云雾信息进行去云
		拉普拉斯滤波法^[40]	将薄云干扰视为高斯模糊噪声，并通过增强影像高频信息来实现去云
	频率域特征滤波（FDFF）	同态滤波法（HF）^[41-42]	假设云雾与地物乘性耦合，通过对数变换将乘性噪声转化为加性噪声，并在频域应用高通滤波器去云
	频率域特征滤波（FDFF）	小波变换法（WA）^[43-46]	通过在不同尺度上分别抑制低频云雾信息并增强高频地表信息来实现去云
	成分分析（CA）	独立成分分析法（ICA）^[47-48]	假设云雾和地表信息为线性混合的非高斯独立信号，利用独立成分分析算法分离两者
	成分分析（CA）	主成分变换法（PCT）^[49]	通过最大化信噪比并调整噪声水平来分离云雾与地表信息
	深度学习（DL）	数据集构建	以仿真模拟^[50-52]或人工采样^[53]的方式构建遥感影像薄云去除数据集
		网络架构设计	包括编码器架构^[54-55]和编码-解码器架构^[56-61]
		损失函数设计	包括监督损失函数^{[51，56，62-63]}和无监督损失函数^{[57，61，64-65]}

Tab. 1

Tab. 2

Overview of studies on think cloud removal in optical remote sensing images and representative approaches"

大类	子类	代表性工作	说明
基于空间光谱信息的方法	影像插值	克里金插值法^[71-72]	基于相邻像素光谱相似性，通过插值重建被厚云遮挡的区域
	影像插值	最大后验概率插值^[73]	利用影像的空间自相关性，结合光谱、纹理和边缘信息重建缺失区域
	传播扩散	基于偏微分方程的扩散模型^[74-75]	基于偏微分方程构建扩散模型，通过空间光谱信息的传播扩散来修复缺失区域
	传播扩散	基于深度学习的扩散模型^[76-79]	基于数据驱动的扩散模型，利用统计先验引导信息传播来修复缺失区域
	范例法	像素级范例法^[80]	通过检索并复制与缺失区域相似的像素来完成影像修复
	范例法	补丁级范例法^[81-83]	通过检索并替换缺失区域的相似区域来修复影像
	生成模型	生成对抗网络^[84-86]扩散模型^[76-79]	学习语义信息与视觉表现之间的映射关系来实现去云利用统计先验引导信息传播实现去云修复
基于同源时序信息的方法	时序克隆	直接时序克隆^[87-88]	假设时序差异小，直接用参考影像填补当前影像缺失
		间接时序克隆^[89-92]	通过辐射校正提升两者视觉一致性，再用参考影像的信息来修复当前影像
		时空融合克隆^[93-98]	结合时序影像空间和时序信息，联合插值和信息克隆方法修复影像缺失区域
	时序张量补全	基于时序连续性的矩阵补全^[99]、自适应张量补全^[100]、深度学习先验和低秩张量结合的补全方法^[101]等	利用时序影像的低秩张量表示，从残缺数据提取本质特征以重建完整影像
	时序学习法	基于传统机器学习的方法^[102-107]	基于低层次视觉特征构建稀疏特征映射，恢复完整影像
	时序学习法	基于深度学习的方法^[108-113]	基于深度学习模型建立残缺时序数据与无云影像的复杂映射关系进行去云
基于异源互补信息的方法	异源影像相关性分析法	异源光学影像相关性分析法^[114-116]	分析并利用异源光学参考影像的光谱相关性，从参考影像中提取先验信息进行去云
	异源影像相关性分析法	异源光学-SAR影像相关性分析法^[117-118]	利用光学和SAR影像的非显式相关性来指导基于范例法的图像修复
	异源影像深度融合	基于物理或统计特性的融合方法^[119-122]	分析异源遥感影像在物理或统计特性上的互补性，并通过融合异源影像来生成无云影像
	异源影像深度融合	基于深度学习的融合方法^[123-127]	通过深度学习建立异源遥感影像互补性，并通过融合异源影像得到无云影像
	异源影像翻译	SAR-光学转换^[128-131]	通过深度学习建立SAR与光学影像的映射关系，生成无云光学影像

Tab. 2

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