A Novel Two-Stream Network for Few-Shot Remote Sensing Image Scene Classification

Recently, remote sensing image scene classification (RSISC) has gained considerable interest from the research community. Numerous approaches have been developed to tackling this issue, with deep learning techniques standing out due to their great performance in RSISC. Nevertheless, there is a general consensus that deep learning techniques usually need a lot of labeled data to work best. Collecting sufficient labeled data usually necessitates substantial human labor and resource allocation. Hence, the significance of few-shot learning to RSISC has greatly increased. Thankfully, the recently proposed discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) method has introduced episodic training- and attention-based strategies to reduce the effect of background noise on the classification accuracy. Furthermore, DEADN4 uses deep global–local descriptors that extract both the overall features and detailed features, adjusts the loss function to distinguish between different classes better, and adds a term to make features within the same class closer together. This helps solve the problem of features within the same class being spread out and features between classes being too similar in remote sensing images. However, the DEADN4 method does not address the impact of large-scale variations in objects on RSISC. Therefore, we propose a two-stream deep nearest neighbor neural network (TSDN4) to resolve the aforementioned problem. Our framework consists of two streams: a global stream that assesses the likelihood of the whole image being associated with a particular class and a local stream that evaluates the probability of the most significant area corresponding to a particular class. The ultimate classification outcome is determined by putting together the results from both streams. Our method was evaluated across three distinct remote sensing image datasets to assess its effectiveness. To assess its performance, we compare our method with a range of advanced techniques, such as MatchingNet, RelationNet, MAML, Meta-SGD, DLA-MatchNet, DN4, DN4AM, and DEADN4, showcasing its encouraging results in addressing the challenges of few-shot RSISC.