Semantic segmentation of remote sensing images plays a vital role in urban planning, traffic guidance and other fields. However, high-resolution remote sensing images typically include large and complex scenes and heterogeneous objects, leading to poor segmentation at the edges of objects, which in turn leads to undesirable segmentation of the whole image. Specifically, current semantic segmentation techniques highlight the superiority of CNNs in maintaining local ground details, but they still can't globally build when processing full-geomorphic images. To address these problems, we propose an effective bi-frequency fusion semantic segmentation network (BFNet) for high-resolution remote sensing images. BFNet uses a bi-branch structure, where the low-frequency branch captures low-frequency context information at different scales based on ESwin-Transformer; meanwhile, a pixel-attention mechanism is designed behind the low-frequency branch to select the optimal global context information; The high-frequency branch extracts high-frequency edge information based on stacked CNNs and transverse connections. In addition, to tackle the issue of detail loss caused by the direct fusion of high-frequency and low-frequency information, we designed a boundary fusion module for bi-frequency balancing to enable better segmentation. Our method achieves good performance on two recognized remote sensing datasets, Potsdam and LoveDA, with mIoU of 87.22% on Potsdam and 92.85% on F1. mIoU on LoveDA is 51.37%, which is a relatively good balance in inference speed and accuracy.