# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
###### Abstract  
Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth's water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud data. Unfortunately, the current methods for collecting cloud data mainly rely on manual observation. This paper presents a novel approach to identifying ground-based cloud images to aid in the collection of cloud data. However, there is currently no publicly available dataset that is suitable for this research. To solve this, we built a dataset of surface-shot images of clouds called the SSC, which was overseen by the Macao Meteorological Society. Compared to previous datasets, the SSC dataset offers a more balanced distribution of data samples across various cloud genera and provides a more precise classification of cloud genera. This paper presents a method for identifying cloud genera based on cloud texture, using convolutional neural networks. To extract cloud texture effectively, we apply Gamma Correction to the images. The experiments were conducted on the SSC dataset. The results show that the proposed model performs well in identifying 10 cloud genera, achieving an accuracy rate of 80% for the top three possibilities. ground-based cloud images; cloud genera; identification; convolutional neural networks; texture; Gamma Correction +
Footnote †: journal: Medical Image Analysis  
0000-0002-1319-2231]E. E. E.  
would significantly enhance the consistency of cloud genera data collection for researchers. However, there is currently a lack of research on the automatic identification of cloud types from ground-based images. The current research on cloud classification has primarily been carried out by categorizing satellite images [6], which provide a direct view of cloud cover on the Earth's surface. However, this method is not highly effective in real-time as these satellites do not continuously monitor the same area, and the time required for data transmission is significant. In contrast, some research on cloud classification has mainly utilized cloud images captured from the ground to identify cloud genera. Researchers have proposed using the textural structure of infrared images [7] to perform cloud classification, focusing on features such as mean grey level, edge sharpness, and cloud gaps. While these methods perform well overall, they may not be accurate enough for identifying similar clouds. This is why some research considers adding the spectral information of clouds as features [8]. While fewer studies have utilized ground-based images for classification, this approach is still feasible. In fact, some studies have employed support vector machines (SVM) [9] to classify clouds based on their texture and structural features. Nevertheless, this approach demands high-quality images and may impact classifier performance if the images are captured during nighttime or under conditions of rain or snow. Along with improvements in camera technology, the use of whole-sky images [10] and infrared cloud analysis instruments [11] provide the basis for collecting high-quality ground cloud image data. The progress of machine learning has led to the widespread use of artificial neural networks (ANN) in the field of computer vision (CV), resulting in significant achievements [12]. Numerous scholars are attempting to incorporate ANNs in meteorology, with convolutional neural networks (CNN) garnering significant attention and producing remarkable results [13; 14; 15]. Presently, some researchers have also started to employ CNN as a technique for cloud image identification. Traditional CNN operates as a hierarchical framework for feature extraction. In a CNN, the lower layers extract the cloud's texture (e.g., structure, fringes, etc. ), whereas the higher layers capture more complex semantic information.