## 1 Introduction
[22] introduced an adaptive matching network, concatenating support and query set discriminative features and assigning similarity scores to sample pairs. This method captures a more comprehensive range of image information and cues. The challenge for these methods lies in better representing sample features and measuring class similarity. They need to address the limitations of sample features and potential issues in handling similarity metrics, as shown in Figure 1.  
Figure 1: An illustration of a common issue in similarity computation for few-shot classification tasks using metric learning. The query image is classified as “stadium”, but its true label is “center”, leading to frequent similar misses. This significantly impacts the classification performance in few-shot learning for remote sensing images. Previous studies confirm that effective data augmentation in few-shot learning significantly improves classification accuracy [23]. Data augmentation methods offer advantages by minimizing additional computational costs and being less constrained by training/testing framework designs. In addition to traditional techniques like rotation and color adjustments, researchers have innovated various augmentation methods, broadly categorized into two types. One type involves data generation. For instance, [PERSON] et al. [24] introduced the data augmentation generative adversarial network (DAGAN) model. It extracts image data from a source domain, projects it into a lower-dimensional vector with an encoder, and concatenates the transformed random vector with a decoder to generate augmented images. [PERSON] et al. [25] proposed the adversarial feature hallucination network (AFHN) model, utilizing generative adversarial networks (cWGANs) for dataset expansion in few-shot learning. This model enhances discriminative capability and diversity by adding a classification regularizer and an anticollapse regularizer. [PERSON] et al. [26] presented a GAN-based data augmentation approach generating high-quality training data. Featuring an additional binary classifier in data and feature spaces, this approach controls the generator for optimized training data, improving classification performance. [PERSON] et al. [27] simultaneously employed GAN and U-Net models to create medical images with additional information, elevating few-shot classification task performance. However, a challenge with such methods is the instability in the contribution of generated features to classification performance. The difficulty arises from evaluating whether the newly generated features possess adequate discriminability. Task-specific regularization may lead to the collapse of the synthesis process, resulting in a lack of diversity in generated samples [28]. The other type of method is based on feature enhancement, forming the foundation of this work. These methods assume that knowledge about relationships between samples within known visible categories can be acquired and transferred to unseen categories. Successfully establishing cross-associations between visible categories and learning these relationships allows the application of the knowledge to handle unseen categories with only a few labeled samples. Researchers believe that by increasing sample diversity, we can expand intraclass differences and better define classification boundaries between different categories [29]. Following this rationale, [PERSON] et al. [30] proposed a semantic feature enhancement algorithm. This algorithm utilizes an encoder-decoder model to map samples to a semantic space, learning concepts of samples in the semantic space. By adding noise, extending samples in the semantic space, finding nearest neighbors, and mapping them back to the visual space, the algorithm achieves effective sample augmentation. [PERSON] et al. [31] introduced a label-set operations (LaSOs) network for multilabel few-shot image classification tasks. LaSOs leverage relationships between label sets to extract potential semantic information, forming data augmentation at the feature space level.