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

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%.