Reliability-Aware Feedback-Controlled Fusion for Spatially Disjoint HSI–LiDAR Classification

This study presents a reliability-aware feedback-controlled fusion framework for hyperspectral image (HSI) and LiDAR classification under spatially disjoint conditions. Instead of committing to a fixed fusion configuration, the framework couples a library of fusion operators (geometry-aware fusion, adaptive gating, cross-modal transformer fusion and physics-informed fusion), integrated within a unified GhostConv-based architecture, with a closed-loop controller that evaluates candidate operators on validation data, accepts them only when they improve validation accuracy, and applies feedback-guided residual refinement. Experiments are conducted on Houston2013, Muufl, and Augsburg under a sample-center spatially disjoint protocol, in which training and validation samples are drawn from development blocks that are spatially disjoint from the test blocks, with seven labeled samples per class and ten random partitions. The framework achieves mean overall accuracies of 70.00%, 73.20%, and 66.34%, improving the Baseline-Ghost model on Houston2013, Muufl, and Augsburg; the improvement achieved by closed-loop training is preserved under a patch-disjoint protocol. Budget-matched analyses show that, with seven labels per class, the gain stems mainly from validation-gated closed-loop training and feedback-guided refinement, whereas the benefit of adaptive operator switching emerges as the label budget increases.