Spatial-Spectral Consistency: A Semi-Supervised Approach for Multispectral Scene Classification

Multispectral remote sensing images, with their richer spectral information, can achieve better scene classification performance compared to RGB images. However, high annotation costs remain a significant challenge. To reduce these costs, we propose a spatial-spectral consistency (SSC) semi-supervised learning method that fully leverages abundant unlabeled data and effectively exploits spectral information from multispectral images. Our method employs two branches to extract spatial and spectral features, respectively. The predictions from the two branches for the weakly augmented input are first fused to generate pseudo-labels, which are then used to supervise the branches in predicting the strongly augmented input. Additionally, we introduce a spectral attention module into the network to enhance its ability to extract spectral information. We conduct extensive experiments on the EuroSAT and SEN12MS datasets, demonstrating that our method outperforms other semi-supervised approaches, achieving state-of-the-art (SOTA) performance.