HBSeNet: A Hybrid Bilateral Network for Accurate Semantic Segmentation of Remote Sensing Images

Semantic segmentation of aerial and satellite images plays a crucial role in a wide range of applications and services, catering to the increasing needs of environmental resource management, urban planning, and traffic safety. Many efficient semantic segmentation methods have been proposed by exploiting deep learning techniques with convolution neural networks (CNNs) architectures and self-attention mechanisms to achieve superior accuracy if compared with conventional machine learning-based approaches. In this article, we introduce a hybrid bilateral segmentation network (HBSeNet), a novel semantic segmentation architecture. Inspired by the success of dual-path network models that replace conventional single-branch encoder–decoder architectures, we construct a model with the core idea of combining the context path and the spatial path to optimize both the accuracy and the complexity of deep learning model in the field of remote sensing image segmentation. Moreover, HBSeNet innovates with auxiliary modules designed to enhance its performance, such as sequential atrous convolution, information synthesis module, and bridge for efficient multiscale feature extraction, fusion, and integration. In simulations, our model achieves a global accuracy of 92.04%, a mean intersection-over-union of 83.57%, and a mean boundary-F1-score of 90.23% when evaluated on the ISPRS Potsdam dataset, surpassing the state-of-the-art segmentation models, such as DeepLabV3+, SwinCNN, and ST-UNet.