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Article summary:

1. This article proposes a novel data information quality assessment method, called K-nearest neighbor (KNN) distance entropy, to screen remote sensing images.

2. The proposed semisupervised few-shot classification method based on KNN distance entropy achieves significant improvement under different experimental conditions.

3. 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.

Article analysis:

The article is overall reliable and trustworthy as it provides detailed information about the proposed semisupervised few-shot classification method based on KNN distance entropy and its application in the remote sensing field. The authors provide evidence for their claims by citing relevant research papers and providing experimental results to support their findings. Furthermore, the authors provide an intuitive interpretation of their results through visualizing the feature distribution of screened data.

However, there are some potential biases that should be noted in this article. Firstly, the authors do not explore any counterarguments or alternative methods that could be used for screening remote sensing images. Secondly, there is no discussion of possible risks associated with using this method which could lead to inaccurate results or incorrect conclusions being drawn from the data. Finally, while the authors present both sides of the argument equally in terms of discussing existing methods and proposing their own solution, they do not discuss any potential drawbacks or limitations of their proposed method which could lead to a one-sided reporting of their findings.