DBCG-Net: Dual Branch Calibration Guided Deep Network for UAV Images Semantic Segmentation

Unmanned aerial vehicle (UAV) remote sensing images used for semantic segmentation possess distinct features compared to urban street scene images, including high resolution and a complex background. Spatial information plays a pivotal role in enhancing the performance of semantic segmentation for high-resolution images. The dual-branch architecture for semantic segmentation incorporates supplementary branches to capture spatial information. However, prior research on dual-branch semantic segmentation neglected the interaction between the contextual and spatial branches, leading to suboptimal model performance. In this discourse, the paper introduces a dual-branch semantic segmentation framework. This design advances the system's understanding of spatial information while facilitating inter-branch learning through two key modules. Initially, the spatial calibration feature extraction module employs frequency domain processing and learning tactics distinct from the contextual approach to generate image features under varied noise conditions. Calibration is achieved by generating features from diverse angles. Subsequently, the spatially-guided loss function directs the acquisition of spatial information for the spatial branch by condensing the deep image characteristics for the context branch. To assess the generalization capacity of the proposed method, experiments will be conducted on three different datasets. The proposed method's modules will be integrated into three representative dual-branch networks, allowing assessment of the generalization capacity of the key DBCG components. Empirical evidence demonstrates that this approach is highly effective, significantly surpassing the performance of the baseline network.