A Flexible Framework for the Spatial Extension of Hyperspectral Classification Maps Using Multispectral Data

Hyperspectral (HS) sensors offer high spectral discrimination but generally limited spatial coverage, whereas multispectral (MS) sensors provide broad coverage with lower spectral detail. HS-MS complementary mapping aims to reduce coverage gaps in HS sensors by training classifiers within HS-MS overlap regions and applying them to surrounding MS-only areas. However, comprehensive comparisons across classifiers remain scarce in this complementary mapping setting. Furthermore, lightweight pixel-wise classifiers often produce spatially inconsistent predictions, whereas spatial–spectral deep learning models demand substantial computational resources. In this study, we propose a flexible HS-MS complementary mapping framework by systematically evaluating 13 classifiers across mineral and land-use/land-cover (LULC) mapping tasks and introducing class-adaptive uncertainty revocation (CAUR), a lightweight, classifier-independent post-processing module. Performance was evaluated via spatial holdout cross-validation within the HS-MS overlap area. When computational resources are sufficient, 3D convolutional neural networks (3D-CNN) achieve the highest accuracy. Conversely, lightweight models such as random forest (RF) and k-nearest neighbors (kNN) provide computationally efficient and robust baselines. Applying CAUR consistently improves spatial consistency and overall classification accuracy without model retraining, with the largest improvements observed for coarser-resolution HS reference data. These findings provide practical design guidelines for constructing efficient HS-MS complementary mapping pipelines tailored to application demands and sensor characteristics.