## 1 Introduction  
Remote sensing images are widely used in economic construction, national defense construction, and people's daily life. Semantic segmentation of remote sensing images is an important aspect of remote sensing image processing, which plays a crucial role in urban planning, disaster relief, traffic management, and climate modeling. Semantic segmentation of remote sensing images refers to the process of assigning specific class labels to individual pixels in an image, enabling a comprehensive understanding of the image content [1]. The classified objects include buildings, roads, vegetation, etc. Deep learning-based semantic segmentation algorithms have become the mainstream for remote sensing image processing. According to the difference in network structure, existing networks fall into three main categories: convolutional neural network(CNN)-based, Transformer-based, and hybrid CNN-Transformer models. In terms of CNN-based semantic segmentation, classical convolutional neural networks include FCN [2], U-Net [3], DeepLab [4; 5; 6], PSPNet [7], etc. FCN [2] represents the inaugural convolutional neural network to attain image semantic segmentation. Iteliminates the fully connected layer from the conventional classification network and effectively transforms the image classification network into an image segmentation network. This is achieved by reinstating the resolution of the feature map through deconvolution. U-Net [3] is a symmetrical network structure of encoder and decoder, which makes full use of the multi-scale image convolution features generated by the encoder through skip connections. DeepLab uses atrous convolution to solve the problem of spatial resolution degradation in the downsampling process of traditional CNN networks. To enhance the DeepLab networks, many researchers have incorporated depthwise separable convolution, parallel atrous separable convolution, and symmetric encoder-decoder structures. This integration has resulted in the development of more advanced variants. PSPNet [7] designs a new method of multi-scale feature calculation and usage, namely using four pooling layers of different sizes to generate feature maps of different levels and then obtaining image features that fuse multi-scale information through feature map aggregation, so as to improve the global information mining ability. Inspired by the idea of feature map accumulation in ResNet networks [8], DenseNet [9] improves feature reuse through denser connections and slows down gradient vanishing. In addition to the network structure, many attempts have also been made to improve the semantic segmentation of remote sensing images. These methods include the use of the attention mechanisms to enhance feature representation [10; 11; 12; 13; 14; 15; 16], generative adversarial network to enable cross-domain semantic segmentation [17; 18; 19], a self-updating CNN model [20] and a progressive edge guidance network [21] to incorporate geographic knowledge into CNN models, a semantic category balance-aware involved anti-interference network named SCBANet [22] to handle category imbalance issue, a high-order semantic decoupling network (HSDN) to disentangle features [23], a uncertainty-aware network (UANet) [24] to facilitate level-by-level feature refinement. However, since the convolution operation is only performed within a certain range around the center point, the range of the receptive field is limited, and there is a lack of global understanding of the image itself. To make full use of the context information of images, some researchers have introduced the Transformer [25] structure with global modeling capabilities into the field of image processing, and a series of image semantic segmentation networks based on Transformer have emerged, such as Vision Transformer (ViT) [26], Swin Transformer [27], etc. ViT [26] is the first semantic segmentation network based on Transformer structure.