遥感影像云检测和云去除方法综述

Review of Cloud Detection and Cloud Removal Methods for Remote Sensing Images

遥感影像是目前可以大范围获取海洋、大气和地球表面信息的数据资源,在农业、军事和城市规划等各个领域发挥重要作用.但是在影像观测过程中会受到云雾等污染因素的影响,导致遥感影像信息缺失,在实际应用中造成巨大的资源损失和浪费.因此,如何对遥感影像云雾覆盖区域进行检测并对其进行校正和修复是国内外专家广泛关注的具有挑战性的难点问题.全面综述其研究进展,总结了现有遥感影像云层检测和去除的挑战;根据是否利用深度学习技术将云检测方法分为2大类,根据是否利用辅助影像将云去除方法分为3大类,依照不同方法特性系统分析和对比了其基本原理和优缺点;基于上述总结在2组遥感影像公开数据集上分别对4种云检测、4种薄云去除和4种厚云去除方法进行了性能评测;最后讨论了本领域目前仍存在的问题,对未来研究方向进行了预测,希望能够对该领域研究人员提供有价值的参考.

Remote sensing images are the data resource that can acquire information about the ocean,atmosphere,and the earth’s surface,and have been widely applied in many fields,such as agriculture,military,and urban planning.However,clouds and hazes are inevitable factors when collecting images from satellites,resulting in the loss of information and causing a huge waste of data resources in practical applications.Therefore,how to detect and remove clouds from remote sensing images is a challenging and difficult task that draws a lot of experts’attention.We comprehensively review current research progress and summarize the challenges of cloud detection and removal in remote sensing images.Cloud detection methods are divided into two categories based on whether using deep learning technology,and cloud removal methods are divided into three categories based on whether auxiliary images are used.Then,according to the characteristics of different methods,these methods are reviewed and analyzed systematically,including their advantages and disadvantages,respectively.Afterward,four cloud detection,four thin cloud removal and four thick cloud removal methods are evaluated on two remote sensing datasets.Finally,we discuss future challenges and predict future research directions.This review paper can provide valuable advice to scientists who are involved in remote sensing image processing.