Class-Specific Interpretation and Validation of Optical and SAR Sensor Contributions in Multi-Sensor Land-Cover Classification Using SHAP and Leave-One-Sensor-Out Ablation

Abstract

This study benchmarks four classifiers—Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Gradient Tree Boosting (GTB)—on an identical 16-feature KOMPSAT-3/5 and Sentinel-1/2 stack harmonized to a common 2.8 m grid within Google Earth Engine. RF and GTB reached overall accuracies of 92.4% and 92.8%, separating them from the unconstrained CART under family-wise correction and from the linear-kernel SVM under false-discovery-rate control. Class-specific SHAP was cross-interpreted against permutation importance, feature-correlation analysis, and a leave-one-sensor-out (LOSO) ablation, with SHAP and permutation-importance rankings in broad agreement (Spearman ρ = 0.72–0.92). The central contribution is to characterize two conditions—strong within-sensor collinearity and class-conditional information concentration—under which feature-level attribution and sensor-level necessity diverge. In the Bare Land class, no individual Sentinel-2 SWIR band ranks among the top three by SHAP, yet withholding the Sentinel-2 group produces the largest class-wise degradation observed (ΔF1 = 0.102 for RF and 0.130 for GTB), consistent with attribution dilution across the collinear SWIR1–SWIR2 pair (r = 0.97). Because none of the sixteen ablation contrasts survives multiplicity correction with a sample size of 250 (n = 250), these contrasts are reported as effect-size estimates rather than confirmatory tests.