| Summary: | Compared with the traditional method based on hand-crafted features, deep neural network has achieved a certain degree of success on remote sensing (RS) image semantic segmentation. However, there are still serious holes, rough edge segmentation, and false detection or even missed detection due to the light and its shadow in the segmentation. Aiming at the above problems, this article proposes a RS semantic segmentation model SCG-TransNet that is a hybrid model of Swin transformer and Deeplabv3+, which includes Swin-Conv-Dspp (SCD) and global local transformer block (GLTB). First, the SCD module which can efficiently extract feature information from objects at different scales is used to mitigate the hole phenomenon, reducing the loss of detailed information. Second, we construct a GLTB with spatial pyramid pooling shuffle module to extract critical detail information from the limited visible pixels of the occluded objects, which alleviates the problem of difficult object recognition due to occlusion effectively. Finally, the experimental results show that our SCG-TransNet achieves a mean intersection over union of 70.29<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the Vaihingen datasets, which is 3<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher than the baseline model. It also achieved good results on POSDAM datasets. These demonstrate the effectiveness, robustness, and superiority of our proposed method compared with existing state-of-the-art methods.
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