Automatic large-scale mapping of land cover classes facilitates applications in sustainable development, agriculture, and urban planning, and is therefore a commonly studied topic in remote sensing image processing, but typical deep learning approaches use models pre-trained on everyday image datasets like ImageNet and retrain them using only three channels (usually RGB), not fully leveraging the unique properties and spectral information of multispectral satellite images.