Scale-Wise Interaction Fusion Network for Land Cover Classification of Urban Scene Imagery

Accurate land cover classification of urban aerial imagery presents significant challenges, particularly in recognising small objects and similar-appearing features (e.g., flat land, prepared land for cultivation, crop growing areas and built-up regions along with ground water resource areas). These challenges arise due to the irregular scaling of extracted features at various rates from complex urban scenes and the mismatch in feature information flow across channels, ultimately affecting the overall accuracy (OA) of the network. To address these issues, we propose the scale-wise interaction fusion network (SIFN) for land cover classification of urban scene imagery. The SIFN comprises four key modules: multi-scale feature extraction, scale-wise interaction, feature shuffle-fusion and adaptive mask generation. The multi-scale feature extraction module captures contextual information across different dilation rates of convolutional layers, effectively handling varying object sizes. The scale-wise interaction module enhances the learning of multi-scale contextual features, while the feature shuffle-fusion module facilitates cross-scale information exchange, improving feature representation. Lastly, adaptive mask generation ensures precise boundary delineation and reduces misclassification in transitional zones. The proposed network significantly improves boundary masking accuracy for small and similar objects, thereby enhancing the overall land cover classification performance.