# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segmentation Method Category** & **Reference** & **Technique** & **Comment** \\ \hline \multirow{8}{*}{threshold-based method} & [5] & \multirow{2}{*}{Red–blue ratio method} & \multirow{2}{*}{Dependent on weather conditions and spectral characteristics} \\ \cline{2-2} \cline{4-5}  & [6] & & \\ \cline{2-2} \cline{4-5}  & [7] & & \\ \cline{2-2} \cline{4-5}  & [8] & & \\ \cline{2-2} \cline{4-5}  & [9] & & \\ \cline{2-2} \cline{4-5}  & [10] & HYTA & \begin{tabular}{c} Solar halo region is easily misidentified as clouds \\ \end{tabular} \\ \hline \multirow{4}{*}{traditional machine learning-based method} & [11] & Superpixel method & \begin{tabular}{c} Over-segmentation and multiple \\ connected regions \\ \end{tabular} \\ \cline{2-2} \cline{4-5}  & [12] & MLP, SVM & Segmentation accuracy needs to be improved \\ \cline{2-2} \cline{4-5}  & [13] & K-Means & Algorithm converges slowly \\ \hline \multirow{8}{*}{Deep learning method} & [14] & U-Net & \begin{tabular}{c} Better segmentation accuracy, complex \\ network \\ \end{tabular} \\ \cline{2-2} \cline{4-5}  & [15] & CloudSegNet & \begin{tabular}{c} Model structure simplification; segmentation \\ accuracy needs to be improved \\ \end{tabular} \\ \cline{1-1} \cline{2-2} \cline{4-5}  & [16] & EFCN & \
\begin{tabular}{c} Low utilization rate of global information \\ and local information \\ \end{tabular} \\ \cline{1-1} \cline{2-2} \cline{4-5}  & [17] & CloudU-Net &
\begin{tabular}{c} Better than EFCN, but segmentation \\ efficiency is not high \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An introduction to the main categories and methods of ground-based cloud segmentation. 1. A dilate block is constructed to extract shallow cloud image features while retaining the fine structure of the cloud image, reducing the loss of cloud information, and capturing a larger range of contextual information. 2. A depthwise separable residual structure DS path is constructed. By stacking depthwise separable convolutions multiple times, the burden on the network is reduced while enhancing the fine-grained representation capability of features. The residual structure allows the encoder input features to be directly transmitted to the output, preserving the original details and combining them with deep semantic features, thereby reducing feature loss. 3. The CBAM attention is improved by constructing an Im-CSAM. The Im-CSAM consists of an improved channel attention mechanism (CAM) and an improved spatial attention mechanism (SAM). The Im-CSAM replaces the fully connected layers in traditional structures with depthwise separable convolutions, achieving more efficient feature extraction and enhancing the modeling capability of the network, thereby improving the CAM; it introduces the computation method of Query, Key, and Value, and uses multiple heads to compute attention independently, enhancing the SAM's ability to model local and global features. 4. The bicubic interpolation up-sampling method is used to restore image resolution and further improve the clarity of segmented images. Bicubic interpolation uses the pixel neighborhood data of \(4\times 4\) around the target pixel point to estimate the value of the target pixel by constructing a smooth interpolation function, thereby achieving high-quality results during image scaling.