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The applied research in remote sensing images has been pushed by convolutional neural network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model global semantic relevance. Modeling global semantic information is possible with the self-attentive Transformer-based model. However, the method of patch computation used by Transformer for self-attentive computation ignores the spatial information inside each patch. To address these issues, we offer the STransFuse model as a new semantic segmentation method for remote sensing images. It is a model that combines the benefits of Transformer with CNN to improve the segmentation quality of various remote sensing images. We employ a staged model to extract coarse-grained and fine-grained feature representations at various semantic scales, unlike earlier techniques based on Transformer model fusion. In order to take full advantage of the features acquired at different stages, we designed an Adaptive Fusion Module (AFM). This module adaptively fuses the semantic information between features at different scales employing a selfattentive mechanism. The OA of our proposed model on the Vaihingen dataset is 1.36% higher than the baseline, and 1.27% improvement in OA over baseline on the Potsdam dataset. When compared to other advanced models, the STransFuse model performs admirably.

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