# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 1 Introduction  
Clouds are a common and important natural phenomenon consisting of water droplets, ice crystals, or a mixture of both, forming various shapes that float in the sky, covering more than 60% of the total land area on Earth [1]. Clouds play an important role in maintaining the atmospheric radiation budget balance by suppressing both shortwave and longwave solar radiation. Additionally, clouds also play a significant role in Earth's heat balance and water vapor cycle; they not only reflect current atmospheric movements and water vapor changes but also play a crucial role in regulating and indicating future weather and climate changes, making them one of the key factors influencing global climate change [2]. Therefore, accurately obtaining sky information through cloud observation is of positive significance for the fields of meteorology [3] and astronomy [4]. Since ancient times, cloud observation has been a very important and challenging task. With the continuous invention and improvement of various hardware devices and instruments, the method of cloud observation is gradually shifting from manual observation to automated observation. Automated observation is based on imaging devices that simulate the human eye for cloud observation. Currently, automated observation is mainly divided into satellite remote sensing and ground-based remote sensing. Satellite remote sensing primarily uses satellite equipment to obtain multi-band images; ground-based remote sensing mainly uses total sky imagers or panoramic fisheye cameras, and the obtained images are ground-based cloud images. However, satellite cloud observation can only capture information about the tops of clouds, and the description of mid-lower layer clouds or cloud base information is not clear enough, leading to satellite cloud observation currently not meeting the needs of relevant scientific research. In contrast, ground-based cloud images mainly reflect the local area of the sky and the distribution, change, and movement of cloud layers, allowing for effective and accurate acquisition of information about cloud cover, cloud height, and cloud type. Therefore, ground-based observation is an important part of research related to cloud observation. Cloud image segmentation is the first step in cloud observation. In the fields of computer vision and remote sensing, cloud image segmentation aims to accurately extract cloud regions from Earth observation images and classify each pixel of the image into cloud or non-cloud categories. It is roughly divided into three categories: threshold-based methods, traditional machine learning-based methods, and deep learning methods. Thresholding is one of the most commonly used and simplest methods in traditional image segmentation. Traditional threshold segmentation is often performed by the relative difference between the red channel and the blue channel in the three RGB channels of an image. [PERSON] et al. [5] and [PERSON] et al. [6] proposed a fixed threshold algorithm which uses the ratio of red and blue channel values to achieve cloud-sky recognition from cloud images. Similarly, [PERSON] et al. [7] used the red and blue difference as a fixed threshold, where the segmentation results are better than the red and blue ratio method but still produce a small error. However, considering the complexity of cloud images, such algorithms are not effective in segmenting thin clouds. [PERSON] et al. [8] proposed an adaptive threshold segmentation method based on the OTSU algorithm. By comparing the adaptively calculated threshold with the grayscale value after processing the blue and red bands, the sky and cloud pixels are distinguished. However, cloud cover can severely affect the accuracy of adaptive thresholds. Based on the adaptive threshold method, a ground-based cloud image segmentation method based on local threshold interpolation has been proposed [9]. [PERSON] et al.