Training-Free Lightweight Transfer Learning for Land Cover Segmentation Using Multispectral Calibration

Highlights: What are the main findings? Response Surface Methodology-based channel calibration achieves up to 67.86 percentage points of IoU improvement for coniferous forest with low baseline performance, while yielding a 59.92 percentage points IoU gain in non-targeted agricultural land, demonstrating cross-class benefits without GPU-based retraining. Class-wise optimal hyperparameters transfer across domains via proportional mapping, proving that generalization is possible between French coastal/mountainous areas and Korean data. What are the implications of the main findings? The proposed training-free approach enables practical transfer learning in resource-constrained environments using only 30–150 labeled tiles instead of thousands required for conventional fine-tuning, with minimal cost and effort. The reproducible relationship between RGB channel statistics and segmentation performance suggests that CNN internal representations form structured manifolds proportional to input spectral characteristics, advancing the interpretability beyond traditional “black box” paradigms. This study proposes a lightweight framework for transferring pretrained land cover classification architectures without additional training. The system utilizes French IGN imagery and Korean UAV and aerial imagery. It employs FLAIR U-Net models with ResNet34 and MiTB5 backbones, along with the AI-HUB U-Net. The implementation consists of four sequential stages. First, we perform class mapping between heterogeneous schemes and unify coordinate systems. Second, a quadratic polynomial regression equation is constructed. This formula uses multispectral band statistics as hyperparameters and class-wise IoU as the dependent variable. Third, optimal parameters are identified using the stationary point condition of Response Surface Methodology (RSM). Fourth, the final land cover map is generated by fusing class-wise optimal results at the pixel level. Experimental results show that optimization is typically completed within 60 inferences. This procedure achieves IoU improvements of up to 67.86 percentage points compared to the baseline. For automated application, these optimized values from a source domain are successfully transferred to target areas. This includes transfers between high-altitude mountainous and low-lying coastal territories via proportional mapping. This capability demonstrates cross-regional and cross-platform generalization between ResNet34 and MiTB5. Statistical validation confirmed that the performance surface followed a systematic quadratic response. Adjusted R² values ranged from 0.706 to 0.999, with all p-values below 0.001. Consequently, the performance function is universally applicable across diverse geographic zones, spectral distributions, spatial resolutions, sensors, neural networks, and land cover classes. This approach achieves more than a 4000-fold reduction in computational resources compared to full model training, using only 32 to 150 tiles. Furthermore, the proposed technique demonstrates 10–74× superior resource efficiency (resource consumption per unit error reduction) over prior transfer learning schemes. Finally, this study presents a practical solution for inference and performance optimization of land cover semantic segmentation on standard commodity CPUs, while maintaining equivalent or superior IoU.