## 1 Introduction  
Climate change has been impacting increasingly both the environment and human activities. Continuously monitoring the dynamic change of Earth's climate, temporally and spatially, has attracted significant efforts. Clouds are the most uncertain but important factor in Earth's climate system. According to the Global Energy and Water Cycle Experiment Cloud Assessment Dataset, the global average cloud coverage is about 68% per year [1, 2]. By affecting solar radiation transfer and precipitation, clouds can shape the energy and water cycles of the global ecosystem at multiple scales. Therefore, investigating the shape of clouds will facilitate the study of the climate of the corresponding region. The accurate detection and segmentation of clouds is of great significance for identifying different weather systems, for meteorological forecasting, and for preventing natural disasters. Meteorological satellites can observe the cloud coverage on Earth from top to bottom. Satellite images are an important source of cloud data. The semantic segmentation of clouds is a challenging task during the processing of satellite cloud images. Over the years, multiple classes of methods have been developed for image segmentation. The threshold-based image segmentation method [3, 4, 5] classifies image pixel points into several classes by setting different feature thresholds. The superpixel-based image segmentation method [6, 7] aggregates neighboring pixels with similar texture, color, luminance, and other features into a certain block of pixels to achieve the purpose of replacing a large number of pixels with a small number of blocks. Watershed-based image segmentation methods [8, 9] use similarity between neighboring pixels as an important reference, so that pixels with similar spatial locations and similar grayscale values are connected to form a closed contour. Level-set-based image segmentation methods [10, 11] use a numerical technique for interface tracking and shaping a geometric activity profile model. However, traditional machine learning-based image segmentation methods have difficulty meeting the demand for highly accurate, robust, and portable methods. In recent years, deep learning-based image segmentation methods have made great breakthroughs and have gained the attention of researchers. Many excellent models and architectures have been widely applied in visual recognition systems and have achieved great results, including satellite remote sensing image detection, medical image analysis, video surveillance, etc. There are various solutions proposed for the semantic segmentation of satellite cloud images. However, most of them are based on the direct enhancement of features, such as using attention mechanisms [12] and stronger backbones to enrich the semantic information; they do not take advantage of the fact that clouds are easily distinguished from the background, while there is little difference between different types of clouds. In this paper, we propose an improved MaskRCNN [13] framework for the semantic segmentation of satellite images. We design a deconvolution decoder composed of a parallel convolution block and a spatial attention deconvolution block. The deconvolution decoder is used to extract the binary semantic segmentation mask results that distinguish the foreground and the background, thereby suppressing the output of false-positive masks, refining the boundary information of semantic segmentation, and improving the pixel-wise accuracy of the semantic segmentation of the satellite cloud layer. We also explore two architectures of deep neural networks that use auxiliary loss and feature fusion, and conduct comparative experiments on the dataset called \"Understanding Clouds from Satellite Images\" [14] from the [PERSON] competition. More importantly, the two neural network architectures proposed in this paper can be widely applied to various semantic segmentation neural network models to improve the distinction between the foreground and the background.