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Semantic segmentation of remote sensing images is increasingly important in urban planning, autonomous driving, disaster monitoring, and land cover classification. With the development of high-resolution remote sensing satellite technology, multilevel, large-scale, and high-precision segmentation has become the focus of current research. High-resolution remote sensing images have high intraclass diversity and low interclass separability, which pose challenges to the precision of the detailed representation of multiscale information. In this paper, a semantic segmentation method for remote sensing images based on Swin Transformer fusion with a Gabor filter is proposed. First, a Swin Transformer is used as the backbone network to extract image information at different levels. Then, the texture and edge features of the input image are extracted with a Gabor filter, and the multilevel features are merged by introducing a feature aggregation module (FAM) and an attentional embedding module (AEM). Finally, the segmentation result is optimized with the fully connected conditional random field (FC-CRF). Our proposed method, called Swin-S-GF, its mean Intersection over Union (mIoU) scored 80.14&#x0025;, 66.50&#x0025;, and 70.61&#x0025; on the large-scale classification set, the fine land-cover classification set, and the &#x201C;AI &#x002B; Remote Sensing imaging dataset&#x201D; (AI&#x002B;RS dataset), respectively. Compared with DeepLabV3, mIoU increased by 0.67&#x0025;, 3.43&#x0025;, and 3.80&#x0025;, respectively. Therefore, we believe that this model provides a good tool for the semantic segmentation of high-precision remote sensing images.

A Semantic Segmentation Method for Remote Sensing Images Based on the Swin Transformer Fusion Gabor Filter