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

• DOI: 10.14358/pers.23-00067r2
• Publication Date: 2024-02-01T04:35:31Z
• Authors: Yuxuan Zhu; Erzhu Li; Zhigang Su; Wei Liu; Alim Samat; Yu Liu
• Keywords: One shot

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