## 4 Conclusions  
The impact of data augmentation on classification performance in few-shot learning is evident. Traditional feature enhancement methods have not explored the distinctiveness of features extensively, leading to unstable gains in classification performance. This issue is particularly common in the context of few-shot tasks with remote sensing images. Even with improvements in data augmentation techniques, it is challenging to provide effective support in the design of learning models. In this paper, we quantified feature distortion magnitudes and projected them onto a feature distortion magnitude space. Through the search of this distortion space, we optimized the distribution of sample features. Subsequently, to fully utilize this distribution, we proposed a classification model based on dual-path classification. The additional classification branch, through learning the comparison of intraclass and interclass similarities of all support samples, reinforced the classification process of the original branch while mitigating, to some extent, the shortcomings of the original branch in classifying challenging data. In the experimental section, we validated the effectiveness of the ODS-DC joint method using general remote sensing datasets. Furthermore, our comparative experiments revealed that the gains brought by the ODS-DC method surpass current State-of-the-Art data augmentation methods. In the ablation experiments, we explored the impact of changes in distortion magnitudes of different features on classification performance. Regrettably, due to hardware constraints and model efficiency, we were unable to conduct more in-depth investigations using higher-dimensional feature spaces comprising various feature types. However, in subsequent investigations, we will not only focus on expanding the dimensions of the feature space but may also introduce additional distortion parameters, thereby further exploring the potential of feature distortion in few-shot classification. Overall, this method's novel and valuable perspective on feature distortion and model optimization offers a more efficient way to utilize data for few-shot classification learning in remote sensing scenes. It also provides new insights into research on data augmentation in deep learning.  
Conceptualization, Z.D. and B.L.; methodology, Z.D.; software, Z.D. and F.X.; validation, Z.D. and F.X.; formal analysis, Z.D.; investigation, Z.D.; resources, Z.D.; data curation, Z.D. and F.X.; writing--original draft preparation, Z.D. and F.X.; writing--review and editing, Z.D. and F.X.; visualization, Z.D.; supervision, Z.D. and B.L.; project administration, Z.D.; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The UCM dataset can be acquired from [[https://vision.ucmerced.edu/datasets/](https://vision.ucmerced.edu/datasets/)]([https://vision.ucmerced.edu/datasets/](https://vision.ucmerced.edu/datasets/)), accessed on 26 September 2023; the AID dataset can be acquired from [[https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets)]([https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets)), accessed on 8 October 2023; the NWPU dataset can be acquired from [[https://gcheng-nwpu.github.io/](https://gcheng-nwpu.github.io/)]([https://gcheng-nwpu.github.io/](https://gcheng-nwpu.github.io/)), accessed on 16 October 2023.  
The authors declare no conflicts of interest.