## 1 Introduction  
In recent years, with the rapid development of remote sensing technology, the amount of available remote sensing data has increased significantly. Semantic segmentation technology in remote sensing images has made many contributions to environmental monitoring, crop cover and type analysis, tree species in forests, building classification in urban space, land use analysis, and other important scenes that promote sustained economic growth and continuous improvement of life quality. As a result, it has significant implications for further study to design an excellent semantic segmentation network in the field of high-resolution remote sensing [1]. With the development of sensor technology, the spatial resolution of remote sensing images is constantly improving, and a lot of rich and important information can be obtained from remote sensing images. But the technology for extracting useful feature information from remote sensing data still has a lot of room for improvement. The semantic information in the actual scene is very complex, and the objects of the same semantic class may use different materials or different construction methods, which lead to the diversity of color, size, shape, and texture; in addition, objects of different semantic classes can present similar features, such as cement roofs, cement sidewalks, and cement roads. This high within-class variability and low between-class variability make segmentation scenes very complicated [2]. At the same time, objects occupying fewer pixels are often covered by others occupying more pixels, resulting in small objects being ignored and leading to a large number of false segments. Most of the traditional semantic segmentation techniques first process the image to extract the semantic features of the image and then segment it. Therefore, the accuracy is limited, and the scope of application is very limited. The deep learning method, which adjusts the feature extraction process based on segmentation results, can extract more abstract, high-level feature information and is more suitable for dealing with complex scenes. As shown in Figure 1, the U-Net series [3] and the DeepLab series [4; 5; 6; 7; 8] have shown strong performance in the semantic segmentation field of computer vision. In remote sensing image processing, researchers have considered the specific characteristics of remote sensing data and improved the classical semantic segmentation network to further improve its segmentation performance [9; 10; 11; 12; 13; 14; 15]. [PERSON] et al. [16] proposed a new G2G network based on the Pix2 Pix network to segment the edges of special-class objects more precisely. This network upgrades the segmentation accuracy of objects of interest, but it is not suitable for complex scenes in remote sensing. [PERSON] et al. [17] proposed a dense residual neural network, DR-Net, which can obtain more high-level semantic feature information for subsequent segmentation by combining the advantages of dense convolutional neural networks and residual networks. However, it suffers from overfitting problems, and its generalization ability has a lot of room for improvement. [PERSON] et al. [18] proposed AFNet, which includes a multipath encoder structure for extracting multipath input features, a multipath attention fusion module for fusing multipath features, and a fine attention fusion module for fusing high-level abstract features and low-level spatial features. And it has achieved excellent performance on the Potsdam dataset, but its performance for small object segmentation needs to be further improved. [PERSON] et al. [2] proposed ResUNet-a, which combines atrous convolution, residual connection, pyramid principle, and multi-task inference theory to mainly alleviate the problem of height imbalance in remote sensing datasets. But its performance still cannot meet the demand for accuracy in practical application scenarios. [PERSON] et al.