Dual-Path Feature Fusion Network for Semantic Segmentation of Remote Sensing Images

Both global contextual information and local texture information are of vital importance for the semantic segmentation of remote sensing images due to the high spatial resolution of remote sensing images and large variations in intraclass object size. In this letter, we propose a novel dual-path feature fusion semantic segmentation network for remote sensing images. A pure convolutional module called dual-path feature extraction (DPFE) module is applied to model global contextual and local texture features simultaneously with low complexity. Inspired by ConvNeXt with comparable global contextual modeling capacity with Transformer, the global path of DPFE draws some successful strategies of ConvNeXt to generate powerful global feature. Meanwhile, an attention feature fusion (AFF) module is proposed, which achieves the global and local feature comprehensive fusion by exploring the correlation of channels through attention mechanism. The proposed network is evaluated on Vaihingen and Potsdam benchmarks and the quantitative results show the proposed network can achieve overall accuracy (OA) of 91.3% and 89.7%, respectively, which are better than several representative semantic segmentation approaches.