Multielement-Feature-Based Hierarchical Context Integration Network for Remote Sensing Image Segmentation

In the current remote sensing segmentation tasks, we identify issues of insufficient accuracy in segmenting objects and types with similar colors, along with a lack of adequate smoothness and coherence in edge segmentation. To address these challenges, we propose a network framework called the multielement-feature-based hierarchical context integration network (MHCINet). This framework achieves deep integration of global information, local information, multiscale information, and edge information. First, we introduce an Edge and Levels Grouped Aggregator to fuse shallow features, deep features, and edge information, enhancing foreground saliency. Finally, to better identify instances with similar colors during the feature reconstruction stage, we design a constant multivariate feature integrator to fully exploit multiscale information and global context, thereby improving the segmentation model's performance. Comprehensive experimental results on the Vaihingen and Potsdam datasets demonstrate that MHCINet outperforms existing state-of-the-art methods, achieving mean intersection over union of 84.8% and 87.6% on the Vaihingen and Potsdam datasets, respectively.