## 5 Conclusions  
Shallow cumulus clouds are widely distributed globally. They are sensitive to changes in the environment and have great potential to affect the radiation balance on Earth. The results of semantic segmentations of clouds play a vital role in cloud observations and forecasts, due to their real-time nature and their accuracy. With cloud-cover information, we can update the weather information on time to monitor climate changes, identify different weather systems, and prevent natural disasters such as tornadoes, storms, hail, and floods.  
The existing research in this field does not fully utilize the feature that the characteristics of clouds and backgrounds are very different, while the characteristics of different types of clouds are small. In this research, we design neural networks to explore this feature. We propose a semantic segmentation method according to the four cloud categories in satellite cloud images. We design our models based on MaskRCNN, which is also our baseline model. We add a deconvolution decoder to extract the binary classification message to distinguish the foreground and the background, suppress the output of false-positive masks, refine the boundary information of semantic segmentation, and improve the pixel-wise accuracy of semantic segmentations of satellite cloud images. The deconvolution decoder is composed of parallel convolution blocks and a spatial attention deconvolution block. We also explore two designs of neural network structures using auxiliary loss and feature fusion and compare their performance on the dataset \"Understanding Clouds from Satellite Images\". Compared to the baseline MaskRCNN model, the mIoU of CloudRCNN (auxiliary loss) improves by 15.24%, and that of CloudRCNN (feature fusion) improves by 12.77%.  
Meanwhile, this research has some limitations, which will be addressed in future work. Firstly, the dataset used in this research is from the crowdsourcing platform, roughly labeled, and a part of the background is labeled as clouds. The presence of these mislabeled noise labels can cause the network to optimize in the wrong direction, reducing the robustness of the network. We plan to adopt the noise transition matrix heuristically, or the \"co-teaching\" learning paradigm, to solve this problem. Secondly, the auxiliary branch brings with it complex calculations and more parameters, affecting real-time performance significantly when compared to other lightweight models. We plan to apply some pruning methods or knowledge distillation methods to the model to reduce its computational complexity and improve the real-time capability of satellite cloud segmentation. Thirdly, the labeling of satellite images is challenging and time-consuming. In the future, we could design a semi-supervised method and introduce the same type of unlabeled data to achieve the augmentation of datasets such as the 95-Cloud dataset and the SPARCS dataset, which will further improve the classification performance of the model. Fourth, we mainly validate our model by experimental design, which is deficient in theoretical analysis. In future work, we will carry out work related to model interpretability. Fifth, the number of pixels in the background is often larger than the segmented subject. The loss function can be redesigned and found to solve the class imbalance between foreground and background.  
This research received no external funding.  
Not applicable.  
The dataset supporting the conclusions of this article is available at [[https://www.kaggle.com/c/understanding_cloud_organization](https://www.kaggle.com/c/understanding_cloud_organization)]([https://www.kaggle.com/c/understanding_cloud_organization](https://www.kaggle.com/c/understanding_cloud_organization)) (accessed on 1 June 2021).  
The authors declare no conflict of interest.