# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 1 Introduction
[10] proposed the hybrid thresholding algorithm (HYTA) for ground-based cloud image detection, which takes full advantage of the combination of fixed and adaptive thresholding methods. However, the above methods are greatly affected by factors such as light intensity and imaging quality of the equipment, and lack of robustness. The area around the sun in cloud images is easily misidentified as clouds, leading to excessive segmentation of cloud images and reduced detection accuracy. In addition to the threshold algorithm, [PERSON] et al. [11] proposed a new ground-based cloud image segmentation method that uses a superpixel-based GM (graph model) to integrate multiple information sources. However, this method may result in over-segmentation and multiple connected regions during the iterative process. [PERSON] et al. [12] applied a multilayer perceptron (MLP) and a support vector machine (SVM) to ground-based cloud image segmentation for the first time. This method inputs the pixel values of the red, green, and blue bands of the cloud image. After processing with the MLP and SVM, the output will classify the pixels based on whether the area is covered by clouds. The performance of the segmentation is optimized by continuously adjusting the parameters of the MLP and SVM, but the segmentation accuracy needs to be improved. [PERSON] et al. [13] proposed a segmentation method based on K-means color space. This method first transforms the color of the cloud image and converts the RGB color space into the CIE Lab color space, thereby separating the photometric and chromatic information. However, the centroid can lead to slow convergence of the algorithm when it is chosen completely at random. The abovementioned traditional machine learning cloud image segmentation methods have improved segmentation quality to a certain extent compared to threshold algorithms. However, such methods are still affected by factors such as light intensity, and the quality of cloud image segmentation needs further improvement. With the development of deep learning, deep convolutional neural networks have become a hotspot in the field of vision, and more and more deep learning models are applied to cloud image segmentation tasks. [PERSON] et al. [14] proposed U-Net for image segmentation, which effectively integrates shallow detail information and deep semantic information through skip connection layers. [PERSON] et al. [15] used a lightweight network CloudSegNet to simultaneously complete the two tasks of segmenting daytime and nighttime ground cloud images. [PERSON] et al. [16] proposed an automatic segmentation method for ground-based cloud images based on the enhanced fully convolutional neural network (EFCN). In addition, [PERSON], [PERSON], and [PERSON] [17] proposed a new network Cloud U-Net to segment ground-based cloud images, which performed better than the EFCN. The categories and technical methods used in the above research and literature related to cloud image segmentation tasks are detailed in Table 1.  
However, most of the existing deep learning models ignore the information exchange between low-level and high-level features, and important cloud features in spatial and channel dimensions are undervalued, resulting in poor model training accuracy, poor generalization, and poor segmentation. This paper proposes a cloud image segmentation method that integrates novel convolution and attention mechanisms. By introducing dilated convolutions and depthwise separable convolutions into U-Net, and improving the CBAM attention mechanism, the segmentation accuracy and effectiveness of the model are enhanced. The contributions of this paper are as follows: