# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
###### Abstract
Despite ground-based images having limitations in representing spectral information and cloud composition, the texture features of clouds remain the primary basis for their classification. Ground-based images are better suited to depict this feature [9]. Considering the potentially subtle textural variations between cloud genera in the implementation, CNNs' ability to efficiently extract feature information through convolutional  
Figure 1: Diagram of the water cycle. kernels offers a means to achieve the automatic and precise classification of ground-based cloud images. The DeepCloud network was the first to attempt to use neural networks to identify ground-based cloud images [16]. The researchers classified clouds into eight categories and supplemented them with clear sky images. They then employed deep neural networks to extract features from the images and achieved an accuracy of 80%, demonstrating the efficacy of neural network classification. Following the DeepCloud study, subsequent research has also utilized neural networks to identify ground-based cloud images, known as the CloudNet [17]. In the CCSN database, clouds were classified into 11 categories, and the model was enhanced by normalizing the RGB three-channel values in the images and increasing the image contrast, ultimately resulting in a high level of accuracy. Subsequently, [PERSON] et al. proposed combining neural networks and random forest to further improve the classification performance of the model [18]. [PERSON] et al. proposed MMFN, which could learn extended cloud information by fusing heterogeneous features in a unified framework [19]. TGCN is a model that learns features in a supervised manner and incorporates graph computation into the model [20]. However, these studies have not addressed the problem of accurately classifying similar cloud formations. In summary, there are few studies in the fields of meteorology or image recognition that use neural networks to identify ground-based cloud images, and all these studies encounter the challenge of having inadequate data samples or poor data quality. In response to the above problems, we built a ground-based cloud image dataset and proposed a method based on CNN to identify ground-based cloud images. The main contributions of this research are as follows:  
1. Constructing a ground-based cloud images dataset, known as the SSC, which is the first dataset to ensure roughly equal sample sizes for each cloud genera. The dataset comprises 1092 data samples, categorized into 10 groups. 2. Various processing methods are employed to adjust the contrast and brightness of the images, thereby enhancing the texture of the clouds. By this approach, the efficiency of the model in extracting cloud texture is enhanced, and the model's performance is improved. 3. We employed a new evaluation method for assessing the performance of our model. The method involves categorizing clouds into three types based on their texture (cumulus, stratiformis, and undulatus). While this evaluation method is relatively uncommon in machine learning, it is crucial in the field of meteorology. In reality, these three types of clouds have varying degrees of impact on the climate, which is an important consideration when collecting cloud data [21; 22]. The remainder of this paper is structured as follows: Section 2 provides a brief introduction to our dataset; Section 3 details a method for improving the recognition of ground-based cloud images by the model through image enhancement; Section 4 presents the experimental results and provides a discussion on the feasibility and effectiveness of the proposed approach; Section 5 is where a summary of the research is presented, along with a discussion on future research directions to explore the practical applications of the results.