Semantics-Aware Hierarchical Consensus Learning for Remote Sensing Image Classification

The paper introduces Semantics-Aware Hierarchical Consensus (SAHC), a deep learning framework that incorporates hierarchical-level-specific classification heads and cross-level probability projectors to leverage predefined label hierarchies in remote sensing image classification. SAHC fuses direct and projected predictions into a geometric consensus distribution, enabling self-consistent training and optional hierarchy-aware inference. Experiments on two benchmark datasets demonstrate the method’s effectiveness in guiding network learning and its robustness across varying spectral and spatial resolutions.

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• computer-vision
• benchmarks