A Multi-Task Learning Framework with Enhanced Cross-Level Semantic Consistency for Multi-Level Land Cover Classification

The multi-scale characteristics of remote sensing imagery have an inherent correspondence with the hierarchical structure of land cover classification systems, providing a theoretical foundation for multi-level land cover classification. However, most existing methods treat classification tasks at different semantic levels as independent processes, overlooking the semantic relationships among these levels, which leads to semantic inconsistencies and structural conflicts in classification results. We addressed this issue with a deep multi-task learning (MTL) framework, named MTL-SCH, which enables collaborative classification across multiple semantic levels. MTL-SCH employs a shared encoder combined with a feature cascade mechanism to boost information sharing and collaborative optimization between two levels. A hierarchical loss function is also embedded that explicitly models the semantic dependencies between levels, enhancing semantic consistency across levels. Two new evaluation metrics, namely Semantic Alignment Deviation (SAD) and Enhancing Semantic Alignment Deviation (ESAD), are also proposed to quantify the improvement of MTL-SCH in semantic consistency. In the experimental section, MTL-SCH is applied to different network models, including CNN, Transformer, and CNN-Transformer models. The results indicate that MTL-SCH improves classification accuracy in coarse- and fine-level segmentation tasks, significantly enhancing semantic consistency across levels and outperforming traditional flat segmentation methods.