P_Segnet and NP_Segnet: New Neural Network Architectures for Cloud Recognition of Remote Sensing Images

In recent years, remote sensing images have played an important role in environmental monitoring, weather forecasting, and agricultural planning. However, remote sensing images often contain a large number of cloud layers. These clouds can cover a large amount of surface information. Therefore, an increasing number of cloud recognition methods have been proposed to reduce the impact of cloud cover. There are many difficulties in traditional cloud recognition methods. For example, the threshold method based on spectral features improves the accuracy of cloud detection, but it often leads to omission or misjudgment in cloud detection and depends on prior knowledge. To improve the accuracy and efficiency of cloud recognition, we use deep learning to address cloud recognition problems in remote sensing imagery. We propose a series of methods from the acquisition and production of training datasets to neural network training and cloud recognition applications. This paper describes a realization of cloud recognition of remote sensing imagery based on SegNet architecture. We have proposed two architectures named P_SegNet and NP_SegNet, which are modified from SegNet. Some parallel structures were also employed into the SegNet architecture to improve the accuracy of cloud recognition. Due to these changes, this paper also discusses the impact of the symmetry network structure on the final accuracy. Our proposed method was compared with the well-known fully convolutional neural network (FCNN) approach. The results have demonstrated the feasibility and practicality of using deep learning approach for cloud recognition in remote sensing images.