# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
###### Abstract
[25] proposed a multichannel CNN-based classification method that first extracts cloud objects from large images and then inputs clouds into multiple channels to extract features, thus improving the classification accuracy. [PERSON] et al. [26] developed a dual-guided loss function for GCI to integrate information from different CNNs in the optimization process, thereby improving the discriminative ability of cloud feature representation. [PERSON] et al. [27] presented a GCI classification network called CloudNet, which includes four convolutional layers and two fully connected layers. In addition, some researchers have applied graph convolutional networks (GCN) to the field of GCI classification. [PERSON] et al. [28] treated each cloud image as a node in a graph and then used GCN to aggregate information from the cloud image itself, as well as its connected images, in a weighted manner to extract richer feature information. [PERSON] et al. [29] proposed a classification method based on the context graph attention network (CGAT), which uses the context graph attention layer to learn context attention coefficients and obtain the aggregated features of graph nodes based on these coefficients, solving the problem that the weights assigned by GCN do not accurately reflect the importance of connected nodes. A cloud cannot be accurately described by the visual information contained in an image alone. Therefore, some researchers have fused visual information with nonvisual features obtained during cloud formation for classification, such as air pressure, wind speed, temperature, and humidity. [PERSON] et al. [30] proposed a joint fusion CNN for learning both visual and nonvisual features in one model, which extracts features using ResNet50 [31] and then uses a weighting strategy for integration. [PERSON] et al. [32] developed a multi-evidence multimodal fusion network (MMFN), which uses an attention network to extract local visual features while learning nonvisual features using a multimodal network. In addition, the authors specifically designed two fusion layers to fully fuse the two features. However, most GCI datasets contain only visual information in practice, so the above approach is not universally applicable. The disadvantage of CNN models is that they do not handle global features well and thus lead to underutilization of features. In contrast, the recently emerged Transformer model can extract abundant global information. Transformer was originally proposed by [PERSON] et al. [33] for natural language processing (NLP) problems, and the model introduced a self-attention mechanism to perform global computation on the input sequence. In the field of NLP, Transformer is gradually replacing recurrent neural networks (RNN) [34; 35]. Inspired by this, related works have applied Transformer to image processing, such as DETR for target detection [36] and SETR for semantic segmentation [37]. Meanwhile, many research results have been generated in the field of image classification. [PERSON] et al. [38] input the pixels of an image as a sequence into the Transformer, which achieved better results but had a high computational cost. [PERSON] et al. [39] proposed the Vision Transformer (ViT), a model that reduces computational complexity by first dividing images into patches before feeding them into the Transformer. [PERSON] et al. [40] suggested a knowledge distillation strategy for Transformer that relies on a distillation token to ensure that the student network learns feature information from the teacher network through attention. Due to the success of Transformer in natural image processing, some researchers have applied it to other fields. [PERSON] et al. [41] used a transfer learning strategy to apply ViT to unmanned aerial vehicle (UAV) image classification, and the performance outperformed the state-of-the-art CNN model. [PERSON] et al. [42] proposed a LeViT-based method for classifying asphalt pavement images, which consists of convolutional layers, transformer stages, and classifier heads. [PERSON] et al.