Rethinking Transformers for Semantic Segmentation of Remote Sensing Images

Transformer has been widely applied in image processing tasks as a substitute for Convolutional Neural Networks (CNNs) for feature extraction due to its superiority in global context modeling and flexibility in model generalization. However, the existing transformer-based methods for semantic segmentation of Remote Sensing (RS) images are still with several limitations, which can be summarized into two main aspects: 1) the transformer encoder is generally combined with CNN-based decoder, leading to inconsistency in feature representations; 2) the strategies for global and local context information utilization are not sufficiently effective. Therefore, in this paper, a Global-Local Transformer Segmentor (GLOTS) framework is proposed for semantic segmentation of RS images to acquire consistent feature representations by adopting transformers for both encoding and decoding, in which a Masked Image Modeling (MIM) pretrained transformer encoder is adopted to learn semantic-rich representations of input images, and a multi-scale global-local transformer decoder is designed to fully exploit the global and local features. Specifically, the transformer decoder uses a feature separation-aggregation module (FSAM) to utilize the feature adequately at different scales and adopts a global-local attention module (GLAM) containing Global Attention Block (GAB) and Local Attention Block (LAB) to capture the global and local context information respectively. Furthermore, a Learnable Progressive Upsampling Strategy (LPUS) is proposed to restore the resolution progressively, which can flexibly recover the fine-grained details in the upsampling process. Experimental results on the three benchmark RS datasets demonstrate that the proposed GLOTS is capable of achieving better performance with some state-of-the-art methods, and the superiority of the proposed framework is also verified by ablation studies. The code will be available at https://github.com/lyhnsn/GLOTS.