A Few-Shot Semi-Supervised Learning Method for Remote Sensing Image Scene Classification

Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples. To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple clas-sification models using the augmented samples. Finally, we perform decision fusion of the results obtained from the multiple models to accomplish the image classification task. According to the experi-ments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (ap-proximately 1.70% to 4.33%) compared to existing counterparts.