# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Example Analysis
#### 3.3.3 Comparison of Experimental Segmentation Results  
In order to further prove the superiority of this paper's model in cloud image segmentation tasks, segmentation experiments were conducted using the FCN, U-Net, SegNet, CloudSegNet, and the improved U-Net, with the results shown in Figures 5 and 6. Figures 5 and 6 show ten test cloud images along with their corresponding segmentation binary maps representing the FCN, U-Net, SegNet, CloudSegNet, and improved U-Net. Black represents the sky while white represents the cloud layer. Figure 5a-e are from the SWINySEG dataset, and Figure 6a-e are from the TCDD dataset. Taking the SWINySEG dataset as an example, Figure 5a shows that SegNet exhibits the poorest segmentation performance. Because it fails to clearly delineate the boundaries between the clouds and the sky, it leads to numerous missed detections, with much of the sky being erroneously classified as clouds. CloudSegNet can roughly identify the general area and distribution of the clouds and the sky, but cannot accurately describe and segment the details of cloud edges. The segmentation results of traditional U-Net and the FCN are relatively good, showing a significant improvement compared to SegNet, as they can capture more shape contour information of cloud bodies, and the contour information of cloud layer edges has also been further optimized. However, for the clouds closest to the edge of the image, the segmentation effect of traditional U-Net is poor, making it difficult to distinguish between the sky and the clouds, and the FCN also struggles to achieve a more detailed edge depiction. Figure 5c shows that U-Net and SegNet have rough processing of boundaries, and the features of small cloud patches are not retained. Although the FCN and CloudSegNet can roughly depict the texture details of cloud images, their ability to distinguish between blue sky and small cloud patches is insufficient. The improved U-Net has the best overall performance, with rich segmentation details and clear boundaries, capable of capturing the complex shapes of clouds, and its segmentation results are more aligned with the real cloud patch shapes. In light of the TCDD dataset (Figure 6a-e), the segmentation results produced by the FCN are rather crude, with blurry and inaccurate cloud boundaries. The FCN is suitable for preliminary segmentation, but the preservation of details is poor, leading to missed detections. In comparison to the FCN, U-Net exhibits improved segmentation  
Figure 5: A comparison of segmentation results based on the SWINySEG dataset. (**a–e**) represents five sets of test images and their segmentation results under different models. accuracy, successfully capturing the majority of the cloud's primary regions, albeit with some boundary details missing. Additionally, its performance on small cloud blocks is not as satisfactory. Given that SegNet inherently possesses a lightweight characteristic with fewer model parameters, its segmentation results exhibit significant noise, thus being unable to accurately depict cloud formations. CloudSegNet is an improved version of SegNet, thereby enabling a more accurate depiction of cloud structures. Yet, CloudSegNet does not use fully connected layers and abandons skip connections, resulting in a significant reduction in the number of trainable parameters. Consequently, the segmentation accuracy improvement is limited. The improved U-Net shows the best performance, significantly enhancing its ability to capture small cloud blocks. The segmentation results indicate that the boundaries are clearer, and the shapes and sizes of the clouds are more accurate, with better processing capabilities for background noise compared to other networks. Figure 6: A comparison of segmentation results based on the TCDD dataset. (**a–e**) represents five sets of test images and their segmentation results under different models. Combining Table 3 and Figures 5 and 6, the FCN and SegNet performed poorly, struggling to handle the details and noise in complex cloud images.