## 2 Proposed Method
### Dual-Path Classification  
Currently, the prevailing approach in few-shot classification involves comparing the query to the mean feature vectors of various classes within the support set. Cosine similarity has been demonstrated to be a highly effective method for measuring similarity [38], and it is typically computed using the formula expressed in Equation (3). Here, \(X\) represents the feature vector, \(s\) denotes the support set, and \(\left\|\cdot\right\|_{2}\) signifies the calculation of the L2 norm. \[\cos\theta=\frac{X_{query}^{T}X_{s}}{\left\|X_{query}\right\|_{2}\cdot\left\| X_{s}\right\|_{2}} \tag{3}\]  
In conventional classification, the mean feature vector represents the overall characteristics of the current class, essentially acting as its centroid. However, in few-shot remote sensing image classification tasks, relying solely on the overall features may lead to the loss of distinctive characteristics contributed by individual samples. Furthermore, the negative impact of this situation becomes more pronounced as the number of intraclass samples increases [39]. Through the effective feature expansion discussed in the previous two sections of this paper, each few-shot category now occupies a richer position in the feature space, providing clearer class boundaries. Based on this, we propose a dual-path classification (DC) strategy. The framework of this method is illustrated in Figure 5. In the figure,\(f\) denotes the feature extraction network, which remains entirely consistent with the one used in pretraining. \(A\) and \(B\) are the two-way classification output labels, \(\lambda_{A}\) and \(\lambda_{B}\) represent the weight parameters for the output probabilities, and \(\mu\) indicates the number of samples added for each class after regular augmentation and feature distortion processing. After extracting the features of the support set samples following data augmentation, we introduce an additional branch on top of the existing training branch. The feature vectors of the support set samples, after entering this additional branch, bypass the mean operation and are directly used to calculate their similarity with the query's feature vector, forming a similarity score matrix \(M\). In this matrix, the scores computed between all samples of the same class and the query are randomly shuffled within each row. Each column represents a set of randomly composed samples with all class labels and their calculated similarity scores with the query. In this score matrix, the label corresponding to the highest similarity score is considered the classification result for that branch. Equations (4) and (5) illustrate the calculation process for the two-way outputs. \[P_{A}(y=r|x)=\frac{\exp(\tau\cdot\langle f(x),w_{r}\rangle)}{\sum_{r\prime}\exp (\tau\cdot\langle f(x),w_{r\prime}\rangle)} \tag{4}\]  
Figure 5: The work flow of the dual-path classification framework. Here, \(A\) represents the conventional classification pathway, focusing on overall category features. Branch \(B\) is an additional pathway added to emphasize richer individual features. \[P_{B}(y=r|x)=\frac{\exp(\tau\cdot(Max\langle f(x),\widetilde{w}_{r}\rangle))}{\sum_ {r}\exp(\tau\cdot\langle f(x),\widetilde{w}_{r}\rangle)} \tag{5}\]  
Here, \(x\) represents the feature vector of the query image, \(w_{r}\) stands for the mean feature vector of the label \(r\), \(\langle\cdot\rangle\) denotes cosine similarity calculation, \(\widetilde{w}_{r}\) is a vector within the set of feature vectors corresponding to the support set, labeled as \(r\), and \(\widetilde{w}_{r}\) forms a pair with a random sample from each of the other categories. \(\tau\) represents the temperature hyperparameter, where a lower value of temperature leads to lower entropy, concentrating the distribution in a few high-confidence positions. To compare the classification effectiveness between the two branches through learning, we add weights \(\lambda_{A}\) and \(\lambda_{B}\) at the output of both the original classification branch and the new classification branch.