GLMCNet: A Global-Local Multiscale Context Network for High-Resolution Remote Sensing Image Semantic Segmentation

High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs. A detail-enhanced filtering module (DEFM) is proposed at the end of the encoder to refine the encoder outputs further, thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information. In addition, a global-local multiscale transformer block (GLMTB) is proposed in the decoding stage to enable the modeling of rich multiscale global and local information. We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively. Finally, we propose the semantic awareness enhancement module (SAEM), which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention. Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method. Specifically, our method achieved a mean Intersection over Union (mIoU) of 86.89% on the ISPRS Potsdam dataset and 84.34% on the ISPRS Vaihingen dataset, outperforming existing models such as ABCNet and BANet.