Semantics-Aware Hierarchical Consensus Learning for Remote Sensing Image Classification
This research introduces Semantics-Aware Hierarchical Consensus Learning (SAHC) to improve remote sensing image classification using deep learning. It creates a geometric ensemble by training multiple hierarchical classifiers and fusing their predictions through cross-level projectors. This mechanism ensures self-consistent training and provides better, more coherent predictions across different detail levels.