Semisupervised Few-Shot Remote Sensing Image Classification Based on KNN Distance Entropy

In recent years, remote sensing image processing based on deep learning has been widely applied in many scenes, but the involved deep learning technology requires large-scale labeled data, which has been a practical problem in the remote sensing field. In this study, we proposed a novel data information quality assessment method, called K-nearest neighbor (KNN) distance entropy, to screen the remote sensing images. The evaluation metric was used to assess unlabeled data and assign the pseudolabel, which further constitutes the proposed semisupervised few-shot classification method in this article. The metatask setting was adopted to verify the validity and stability of experimental results. Specifically, the KNN distance entropy metric can be used to distinguish the samples in the core set or boundary set. Experimental results show that the core set samples are more suitable under the few-shot condition, for instance, the metatask average accuracy trained by the core set samples outperforms that by boundary samples by about 18% in the case of 45-ways and 5-shot. The proposed semisupervised few-shot method based on KNN distance entropy achieves significant improvement under different experimental conditions. The visualization of the feature distribution of screened data is shown to provide an intuitive interpretation. This article lays a meaningful foundation for screening and evaluating remote sensing images under few-shot conditions, and inspires the data-efficient few-shot learning based on high-quality data in the remote sensing field.