## 1 Introduction
[19] proposed a Transformer-based decoder and a novel attention mechanism to improve segmentation performance for real-time remote scenes. The experimental results show that it outperforms other state-of-the-art models in terms of segmentation accuracy. But their work on encoders is still slightly inadequate and needs further research. In general, semantic segmentation methods based on deep learning effectively extract construction features and improve segmentation accuracy. But in the complex scene, with the characteristics of high within-class variability and low between-class variability, the edge accuracy of small object segmentation is low, the segmentation detection error is high, the segmentation efficiency is insufficient, and so on, and the further application of the technology is still hindered. In order to solve the problems above, we conducted a comprehensive analysis of advanced technologies in related fields. We find that Transformer contributes greatly to the improvement of accuracy in the field of image segmentation, but its high computational  
Figure 1: This figure shows some classical semantic segmentation models in the fields of computer vision and remote sensing, which are referred to in this paper. cost also brings great difficulties to the practical application of relevant models. In this regard, convolution has many advantages. Later, we discovered that some research on implementing Transformer's strong spatial interaction capability with convolution has achieved good results. However, we found that it is difficult to completely replace Transformer with convolution, so we combined the two to complement each other. Specifically, based on the Transformer [20], atrous convolution [5], and gated convolution [21] models, this paper proposes a global and local feature fusion network with a biencoder (GLFFNet) for high-resolution remote sensing image segmentation. Most importantly, the main contributions of this work are as follows:  
1. In order to solve the problem of high within-class variability of objects of interest in complex remote sensing images, an efficient spatial feature aggregation encoder (SFA encoder) is proposed for feature extraction. The attention module of the Transformer is incorporated into the recursive gated convolution [22] to perform self-attention operations on feature maps, overcoming the limitations of convolutional networks, such as poor global modeling capability and insufficient exploitation of spatial location information. It is used to extract more abundant multi-scale feature information and improve the segmentation accuracy of the whole model. 2. Aiming at the low between-class variability of objects of interest in complex scenes of high-resolution remote sensing and the related problems they raise. In this paper, a spatial pyramid atrous convolutional encoder (SPAC encoder) is proposed to extract shallow feature information from remote sensing images using spatial pyramid structure and atrous convolutional to maximize the retention of local semantic information for each pixel. This is used to improve the model's recognition of small objects. 3. In order to better transfer the weight of the backbone pre-trained on classification datasets to segmentation tasks, this paper proposes an auxiliary training module called Multi-head Loss Block. This module employs multiple semantic attention layers and four lightweight decoders of the segmentation task to fine-tune the weights of each encoder in the backbone. Through this auxiliary model, encoders can extract more abundant multi-scale feature maps for objects of different sizes, which makes the whole training process convergence faster and more stable.