Remote Sensing Image Semantic Segmentation Based on Cascaded Transformer

High-resolution (HR) remote sensing image semantic segmentation plays an important role in Earth's surface. Despite remote sensing image semantic segmentation methods have developed rapidly. However, the land objects types of HR remote sensing images are complex, the features are difficult to be extracted, and the existing deep learning methods are difficult to obtain enough effective features, but there is still room for further improvement in feature representation ability. In respect of the issues above, we propose novel global and local features aggregated network to simultaneously exploit boundary information and capture hierarchical semantic information for remote sensing image semantic segmentation. In addition, a novel loss module is designed according to generative adversarial network (GAN) to enhance the feature representation capability of the model in multiclass segmentation. In the meanwhile, the performance of CTrans
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Net is verified on three public datasets, and good results are obtained.