Relation-Consistency Group Contrastive Learning for Robust Multispectral Remote Sensing Classification

Multispectral remote sensing classification benefits from the complementary information carried by visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) Sentinel-2 bands, yet most deep models process them as a single stacked tensor without explicitly preserving their inter-group relationships. We propose Relation-Consistency Group Contrastive Learning (Group-CL-RC), a robustness-oriented framework combining group-level contrastive alignment with a relation-consistency regularizer defined over a compact VIS–NIR–SWIR similarity descriptor. The method is evaluated on EuroSAT All Bands using four backbones under radiometric drift, spatial masking, K-drop band removal, and compound spectral–spatial corruption (CS2C), and externally validated on Sentinel-2-only SEN12MS under standard and seasonal-shift protocols. Group-CL-RC preserves strong clean performance and yields statistically supported robustness gains over the multispectral-only baseline, with the largest improvements under K-drop and CS2C. SEN12MS supports the transfer of these robustness trends beyond EuroSAT, while showing that gains over standard Group-CL remain perturbation-dependent. Ablation studies further indicate that relation consistency is an effective robustness mechanism, particularly when spectral-group availability is degraded. Relation-deformation diagnostics show that Group-CL-RC primarily reduces decision-level sensitivity to relational distortions rather than uniformly minimizing raw deformation. Overall, inter-group relational geometry provides an interpretable and effective robustness target under controlled structured spectral and spectral–spatial degradation.