RSUS: A Novel Upsampling Layer for Semantic Segmentation Network of Remote Sensing Images

Abstract

Semantic segmentation of remote sensing images (RSIs) often struggles with boundary blurring, structural discontinuity, and category confusion due to limitations in conventional interpolation and dynamic upsampling methods. This article proposes RSUS, a new upsampling layer designed to preserve semantic consistency and spatial structure. RSUS consists of three components: 1) global context vector aggregation (GCVA) for content-aware prediction kernels that introduce global priors into local feature reconstruction; 2) cross-scale anisotropic implicit positional encoding (CAIPE) for direction-sensitive spatial deformation and structural alignment; and 3) adaptive high-frequency structure gating (AHSG) to enhance boundary-related frequency responses. In addition, persistent homology (PH) is used as a topological analysis tool to assess connectivity and structure preservation beyond pixel-level metrics. Extensive experiments on five datasets (International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen, ISPRS Potsdam, LoveDA, UAVid, and Xining) demonstrate that RSUS improves performance across mainstream segmentation networks, outperforming advanced upsampling methods. Analyses show RSUS alleviates structural misalignment, improves category consistency, and recovers fine-grained boundaries in remote sensing segmentation. The source code is available at: https://github.com/Ronin-711/RSUS