CRED: Class-Specific Reasoning Over Typed Evidence with Reliability-Hardness Prioritized Knowledge Distillation for Multisource Remote Sensing Image Classification

Joint classification of hyperspectral imagery (HSI) and LiDAR/SAR data demands the effective exploitation of complementary information across heterogeneous sensors. However, existing fusion methods primarily model modality-specific features or fused representations, while conventional distillation methods directly use teacher predictions as supervision, making it difficult to identify discriminative information and effective distillation signals for different classes and samples. To address these limitations, we propose CRED, a framework for Typed Reasoning over Multimodal Evidence with Reliability- and Hardness-Prioritized Knowledge Distillation. Specifically, Typed Multimodal Evidence Construction (TMEC) separately encodes spectral, spatial, and complementary modality information, organizes heterogeneous sensor representations into explicitly typed evidence, rather than directly collapsing them into a single fused feature representation. Class-Specific Typed Evidence Budgeting (CSTEB) models interactions among evidence types using Mamba, employs type-specific class queries to assess their support for each candidate class, and combines the resulting scores using class- and sample-specific weights. Finally, Reliability- and Hardness-Prioritized Distillation (RH-PD) constructs teacher reliability and student hardness indicators from spatial-block held-out prediction probabilities, and prioritizes distillation samples accordingly, transferring trustworthy teacher knowledge to difficult samples that benefit most from additional supervision. Experiments on three public HSI–LiDAR/SAR datasets demonstrate that CRED consistently outperforms existing methods across multiple evaluation metrics, achieving OA values of 94.06%, 80.60%, and 92.77% on Houston2013, Berlin, and Augsburg, respectively.