LGLFormer: Local–Global Lifting Transformer for Remote Sensing Scene Parsing
Yuting Yang, Licheng Jiao, Lingling Li, Xu Liu, Fang Liu, Puhua Chen, Shuyuan Yang
Abstract
In deep learning, convolutional neural networks (CNNs) and transformers have gained excellent achievements in remote sensing scene parsing. Strong feature representation ability is still a challenge for them. Besides, the complex scenes are still essential challenges for deep learning in remote sensing scene parsing. In this article, an efficient local–global lifting transformer (LGLFormer) framework is proposed to ease the challenges above. It effectively combines CNNs, transformer, and wavelet transform to build a strong local–global (LG) feature representation network. Besides, global feature learning driven by LG adaptive features is proposed based on the 2-D LG adaptive feature extractor (LGAFE) and refined global feature attention module. The 2-D LG lifting feature extractor is inspired by the lifting scheme, which introduces local and global dependency. Furthermore, two LG lifting schemes are proposed, including the series and parallel modes, which can effectively learn LG relations between pixels. Finally, experiments are validated on three remote sensing benchmark datasets. The proposed LGLFormer achieves the state-of-the-art with 99.02%, 99.2%, and 99.48% overall accuracy (OA) on AID, WHU-RS19, and UCM datasets, respectively. In addition, LGLFormer shows good convergence with competitive parameters. The experimental code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yutinyang/LGLFormer</uri> .