## 1 Introduction  
The field of remote sensing image classification holds a pivotal position in various application domains, including disaster detection [1], land use analysis [2], and environmental monitoring [3]. Early remote sensing scene classification methods predominantly relied on manually crafted features, encompassing texture features, structural features, and spectral features [4]. Correspondingly, a multitude of models based on these features emerged, such as the Bag of Words (BoWs) model [5] and sparse coding models [6]. Their fundamental strategies often revolved around enhancing or reducing certain aspects of the image, such as increasing the sparsity of features or reducing redundant image portions, aiming to improve classification performance. These methods are characterized by their simplicity and efficiency [4, 7]. However, as the demand for improved performance has grown, these methods have shown limited feature representation capabilities and low utilization efficiency of data information, constraining their effectiveness in practical applications. With the rapid evolution of parallel computing resources and advancements in artificial intelligence theory, deep learning algorithms have become the predominant trend in remote sensing image classification [8, 9, 10]. This approach involves using deep encoders, convolutional neural networks (CNNs), and similar architectures for end-to-end feature extraction, followed by analysis and processing using appropriate decoders. Several notable advancements have been made in model optimization. For instance, [PERSON] et al. [11]integrated local convolutional attention modules into the backbone network, resulting in significant target-highlighting effects in complex background remote sensing images. [PERSON] et al. [12] introduced network evolution, training, and searching for better network structures using various remote sensing image datasets. [PERSON] et al. [13] employed a target-background separation strategy, using background information beyond the effective target as decision support to enhance distinguishability between target similarity and background difference samples. They also combined texture and morphological features to guide feature learning, effectively reducing the impact of intraclass differences. In practice, optimizing network architectures significantly enhances performance, assuming sufficient labeled data for structural training optimization. However, a major challenge in remote sensing scene classification is the scarcity of annotated data for model training, especially when faced with tasks involving unseen scene types. Few-shot learning, focusing on a limited number of samples, has gained prominence in addressing this challenge. The primary hurdle in few-shot learning is enabling deep models to quickly learn and infer from a small number of samples without extensive training on large-scale datasets [14; 15; 16]. There are two primary approaches to few-shot learning: meta-learning [17] and metric learning [18]. Meta-learning trains classifiers for quick adaptation to new tasks by sharing knowledge across multiple tasks, enhancing few-shot learning. In high-resolution satellite image scene classification, [PERSON] and colleagues introduced a lifelong few-shot learning approach [19], enabling easy adaptation to new datasets. [PERSON] et al. [20] improved intertask relevance by integrating more historical prior knowledge from partial intratask sequences. They also introduced a graph transformer to optimize the distribution of sample features in the embedding space. In contrast, similarity-based methods or metric learning methods are simpler and more effective. The core idea is to cluster similar samples and disperse dissimilar ones by measuring sample similarity. [PERSON] et al. [21] proposed a deep metric learning-based feature embedding model using the nearest neighbor (NN) algorithm as a classifier, addressing classification tasks for high-spectral remote sensing images within and across scenes. [PERSON] et al.