## 1 Introduction
Such approaches introduce varying degrees of distortion to data, making it crucial to ensure that distorted samples maintain or increase discernibility; otherwise, achieving ideal classification performance becomes challenging. On the other hand, accurate delineation of classification decision boundaries depends on sufficient intraclass variance in labeled samples. Therefore, the process of feature enhancement can be understood as actively adding distortion to original features, with these distortions having limitations. Excessive distortion may lead to the loss of discernibility in numerous newly introduced features, increasing the risk of underfitting [32], as illustrated in Figure 2. The impact of image distortion levels on classification accuracy varies when different numbers of images are used for training (e.g., \(10^{3}\) and \(10^{4}\)). The training data are randomly extracted in proportion, and the horizontal axis represents the distortion magnitude level, while the vertical axis shows the ratio of classifier accuracy when using additional distorted data compared to not using it. It is noteworthy that frameworks or data optimization methods for specific tasks often lack generalizability [33; 34]. Currently, a more universally applicable solution is the use of learned data augmentation policies [35]. The limited adoption of these methodsis primarily due to the mostly discrete nature of the search space they construct. Each subpolicy within this space brings inconsistent gains to the model, with variations even far apart. Hence, the generation of these policy combinations is inherently challenging to generalize. Additionally, achieving optimal parameters involves independent and costly search and learning stages, resulting in unstable performance gains [36]. For remote sensing images, few-shot classification tasks are significantly more challenging than those for ordinary object images. Remote sensing images have lower resolutions, less detail, and are more prone to confusion between images, making it relatively difficult to define interclass boundaries. In this work, to maximize the improvement of interclass boundaries in few-shot learning tasks, we first explored the impact of different types of feature distortions on learning and introduced a method to construct a continuous distortion space. Subsequently, we combined feature enhancement with metric learning, incorporating the distortion magnitude of features into the metric learning process. Through this amalgamation, we tried to construct a classification framework with a better generalization performance through the acquirement of more discriminable additional features and the support of an optimized learning network. The specific contributions of this paper are as follows:  
* For few-shot classification tasks in remote sensing images, we propose a data augmentation method based on distortion magnitude optimization. The core idea of this method is to introduce appropriate shifts in the feature space for limited samples across a distortion magnitude space, thereby probing and reconstructing interclass boundaries. This approach assimilates the strengths of feature enhancement and metric learning methods. By constructing a multidimensional feature distortion space and segmenting the search for distortion magnitude, it efficiently identifies the optimal distortion magnitude;
* We propose a dual-path classification strategy that optimizes the classification process by dynamically adjusting decision weights. This strategy is particularly suitable for few-shot classification tasks in remote sensing images, as it simultaneously considers the feature information provided by the overall sample distribution and individual samples, significantly reducing the probability of classification errors. Figure 2: Illustration of how distortion magnitude influences classification accuracy.