Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network

Abstract

Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer aggregates information from the connected nodes of graph in a weighted way. However, the weights assigned by GCN cannot reflect the importance of connected nodes precisely, which declines the discrimination of the aggregated features (AFs). To overcome the limitation, in this article, we propose the context graph attention network (CGAT) for ground-based remote sensing cloud classification. Specifically, the context graph attention layer (CGA layer) of CGAT is proposed to learn the context attention coefficients (CACs) and obtain the AFs of nodes based on the CACs. We compute the CACs not only considering the two connected nodes but also their neighborhood nodes in order to stabilize the aggregation process. In addition, we propose to utilize two different transformation matrices to transform the node and its connected nodes into new feature spaces, which could enhance the discrimination of AFs. We concatenate the AFs with the deep features (DFs) as final representations for cloud classification. Since existing ground-based cloud data sets (GCDs) have limited cloud images, we release a new data set named GCD that is the largest one for ground-based cloud classification. We conduct a series of experiments on GCD, and the experimental results verify the effectiveness of CGAT.