Using Minimum Component and CNN for Satellite Remote Sensing Image Cloud Detection

Hailin Sun; Li Li; Mai XuQinpeng LiZheng Huang

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Using Minimum Component and CNN for Satellite Remote Sensing Image Cloud Detection

机译：Using Minimum Component and CNN for Satellite Remote Sensing Image Cloud Detection

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摘要

Cloud detection is an important part of remote sensing (RS) image preprocessing. For earth observation tasks, the reliability of RS images will be judged based on the presence of clouds. A large number of cloud detection methods have been developed. There are two difficulties for cloud detection. First, it is hard to detect thin clouds and ragged clouds. Second, clouds are hard to distinguish from photometrically similar regions, such as snow. The rise of deep learning has brought new methods to address the above problems. In this letter, we propose a novel end-to-end neural network that detects clouds without additional manual work. Furthermore, we develop an RGB minimum component transformation mechanism that is useful for discriminating clouds from snow. Moreover, we have made our data set public to help others for further research. Our method increases the precision of cloud detection to 93.73.

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《IEEE geoscience and remote sensing letters》 |2021年第12期|2162-2166|共5页
作者
Hailin Sun; Li Li; Mai XuQinpeng LiZheng Huang;
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作者单位

School of Electronic Engineering, Beihang University, Beijing, China;

General System Department, China Center for Resources Satellite Data and Application, Beijing, China;

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正文语种英语
中图分类地球物理学;
关键词
Clouds; Snow; Convolution; Satellites; Feature extraction; Image color analysis; Kernel; Cloud detection; convolutional neural networks (CNNs); image segmentation; remote sensing (RS) image processing;

Using Minimum Component and CNN for Satellite Remote Sensing Image Cloud Detection

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