Cloud detection for satellite cloud images based on fused FCN features

Abstract

ABSTRACT Cloud detection for satellite cloud images is a challenging image processing task owing to the blurring of cloud boundaries, multiplicity, and complexity of cloud types. Currently, the commonly used cloud detection methods include original full convolutional neural network (original FCN), FCN with an 8-pixel stride (FCN- 8s), FCN with a 2-pixel stride (FCN-2s), and so on. However, the aforementioned methods exclusively rely on a single network layer the final layer feature map; thus, shallow cloud image information, such as cloud profile information may not be captured. In this letter, a cloud detection method for satellite cloud images based on fused FCN features is proposed. The proposed method effectively fuses spatial and high-level semantic information, and a voting ensemble strategy is used to improve the accuracy and robustness of cloud detection. Finally, the experimental results demonstrate that the average overall accuracy (OA), average producer’ accuracy (PA), and average user’ accuracy (UA) of the proposed method for multiple training sample sizes and image sizes of the collected Fengyun satellite (FY-2 G) cloud image database increased by 7.15%, 9.04%, and 8.46%, respectively, relative to the average accuracies of the original FCN, FCN-8s, FCN-2s, SegNet, and DeepLabV3 methods.