A Novel Ground-Based Cloud Image Segmentation Method by Using Deep Transfer Learning
Zecheng Zhou, Feng Zhang, Haixia Xiao, Fuchang Wang, Xin Hong, Kun Wu, Jinglin Zhang
Abstract
Cloud segmentation is fundamental in obtaining many parameters of clouds. However, traditional cloud segmentation performs far from satisfactory, due to the fuzzy boundaries and complex textures of clouds. Although deep learning methods have shown superior performance in cloud segmentation, they are constrained by limited labels in ground-based cloud image data sets. This letter established a new Ground-Based Cloud Segmentation (GBCS) data set with 1742 accurately labeled images. Then to evaluate how well deep learning models perform in cloud segmentation, 12 state-of-the-art semantic segmentation networks are selected, among which DeepLabV3+ outperformed all others. Since 1742 images are not enormous, a novel Transfer learning (TL)-DeepLabV3+ model was developed by TL: DeepLabV3+ network was trained with the PASCAL VOC 2012 data set, then retrained in GBCS. TL-DeepLabV3+ showed a high ability of cloud segmentation, scoring the Mean Intersection-over-Union (MIoU) of 91.05% in GBCS and further verified in the UTILITY data set and the Cirrus Cumulus Stratus Nimbus (CCSN) data set.