Building extraction method in complex scenes of remote sensing based on transformer

In the rapid development of technology, remote sensing image acquisition technology has been widely applied in many fields. However, accurately extracting building information from massive remote sensing data in complex scenes remains a huge challenge. This article proposes a remote sensing building extraction method for complex scenes based Transformer. Using transfer learning, the Google pre trained ViT models weight model is used as the pre training model for the automatic building extraction algorithm model in this article. Based on this pre training model, a remote sensing dataset is trained. With the help of a model combining Transformer and U-Net tasks, the prepared dataset and network structure are adjusted and improved to adapt to the characteristics of remote sensing image samples. The experimental results show that TransUNet can better segment and preserve detailed shape information, enjoying both the benefits of high-dimensional global contextual information and the benefits of low dimensional details. This paper uses TransUNet to achieve the first application of Transformer in building extraction from remote sensing images. It not only encodes the global and contextual information of the image as a sequence, but also effectively utilizes the low dimensional features of CNNs through a U-shaped hybrid structure design.