Causal Knowledge Distillation for Robust Remote Sensing Semantic Segmentation

Knowledge distillation (KD) is widely used to compress large remote sensing semantic segmentation models for deployment on resource-constrained satellites. However, variations across satellite sensors and environmental conditions in real-world deployment can induce significant domain shifts between the source domain and unseen deployment domains. Existing KD methods primarily optimize source-domain accuracy while often overlooking robustness under domain shifts. To improve domain generalization in remote sensing semantic segmentation, we revisit KD from a causal perspective and attribute its limited robustness to two sources: domain-dependent noncausal knowledge transferred by the teacher, as well as noncausal shortcuts acquired by the student from source-domain supervision. Specifically, we propose Causal Knowledge Distillation (CKD), comprising Teacher Causal Knowledge Transfer (TCKT) and Student Causal Knowledge Acquisition (SCKA). TCKT learns prompts to refine teacher representations by suppressing domain-sensitive responses, thereby reducing the transfer of noncausal knowledge during distillation. SCKA applies causal frequency intervention to student inputs to mitigate the acquisition of domain-related shortcuts and encourage stable causal representation learning. In addition, CKD can be readily integrated with different distillation objectives without modifying the student architecture. Extensive experiments across multiple domain shifts demonstrate that integrating CKD with existing KD methods consistently improves robustness.