Semi-Supervised Scene Classification for Optical Remote Sensing Images via Label and Embedding Consistency

The utilization of unlabeled samples has contributed significantly to the achievements of semi-supervised methods in optical remote sensing image (ORSI) scene classification. However, existing methods face the challenge of effectively integrating labeled and unlabeled data during model training. To mitigate these challenges, a semi-supervised label and embedding consistency network (SS-LEC) is proposed for OSRI scene classification. Specifically, given an image, SS-LEC enables the high-confidence prediction from a weak-augmentation view consistent with the prediction from a strong-augmentation view, while also ensuring consistency in embeddings derived from middle-augmentation views. Moreover, a soft learning schedule is proposed to strategically focus on varied consistency tasks at different stages of training. Our experiments on two ORSI datasets showcase SS-LEC's superior classification performance over existing semi-supervised methods. Notably, under label-scarce scenarios with only four labeled images per category, SS-LEC achieves classification accuracies of 92.04% on the EuroSAT dataset and 70.19% on the NWPU-RESISC45 dataset. These results set new benchmarks and demonstrating superior classification performance in challenging conditions with limited labeled data.