CloudRCNN: A Framework Based on Deep Neural Networks for Semantic Segmentation of Satellite Cloud Images

Abstract

Shadow cumulus clouds are widely distributed globally. They carry critical information to analyze environmental and climate changes. They can also shape the energy and water cycles of the global ecosystem at multiple scales by impacting solar radiation transfer and precipitation. Satellite images are an important source of cloud data. The accurate detection and segmentation of clouds is of great significance for climate and environmental monitoring. In this paper, we propose an improved MaskRCNN framework for the semantic segmentation of satellite images. We also explore two deep neural network architectures using auxiliary loss and feature fusion functions. We conduct comparative experiments on the dataset called “Understanding Clouds from Satellite Images”, sourced from the Kaggle competition. Compared to the baseline model, MaskRCNN, the mIoU of the CloudRCNN (auxiliary loss) model improves by 15.24%, and that of the CloudRCNN (feature fusion) model improves by 12.77%. More importantly, the two neural network architectures proposed in this paper can be widely applied to various semantic segmentation neural network models to improve the distinction between the foreground and the background.