遥感图像去噪方法研究综述

doi:10.3778/j.issn.1002-8331.2402-0190

摘要/Abstract

摘要： 成像环境的复杂性导致遥感图像中含有多种类型的噪声，通过对这些噪声的去除，可以有效提高后续工作的效率和精度。近年来，针对遥感图像的去噪方法逐渐成为图像处理领域中的研究热点。在吸收国内外众多学者研究工作的基础上，对可见光遥感图像、红外遥感图像和SAR图像的去噪方法进行了系统性总结。介绍了遥感图像中噪声的主要来源及表现形式；列举了可用于遥感图像去噪方法研究的开源数据集和公开数据平台;根据处理域的不同，阐述了传统遥感图像去噪方法的优势和局限性。对基于深度学习的前沿遥感图像去噪方法进行了重点介绍，总结了其主要创新和不足之处。最后，对遥感图像去噪任务所面临的难题和未来发展方向进行了分析与展望。

关键词: 遥感图像, 图像去噪, 数据集, 深度学习

Abstract: The complexity of the imaging environment results in remote sensing images containing many types of noise, and the removal of these noises can effectively improve the efficiency and accuracy of the subsequent work. In recent years, image denoising methods for remote sensing have gradually become a hotspot in the field of image processing. On the basis of absorbing the research of many scholars at home and abroad, the denoising methods for visible remote sensing images, infrared remote sensing images and SAR images are systematically summarized. Firstly, the main sources and manifestations of noise in remote sensing images are introduced. Secondly, public datasets and data platforms that can be used for the study of remote sensing image denoising methods are listed. The advantages and limitations of traditional remote sensing image denoising methods are described according to the processing domain. Then, the cutting-edge image denoising method for remote sensing based on deep learning is highlighted, and its main innovations and shortcomings are summarized. Finally, the challenges and future development directions of remote sensing image denoising task are analyzed and prospected.

Key words: remote sensing image, image denoising, datasets, deep learning

王浩宇, 杨海涛, 王晋宇, 周玺璇, 张宏钢, 徐一帆. 遥感图像去噪方法研究综述[J]. 计算机工程与应用, 2024, 60(15): 55-65.

WANG Haoyu, YANG Haitao, WANG Jinyu, ZHOU Xixuan, ZHANG Honggang, XU Yifan. Review of Image Denoising Methods for Remote Sensing[J]. Computer Engineering and Applications, 2024, 60(15): 55-65.

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