## 1 Introduction
Recent studies suggested that the development of hybrid DL architectures has the potential to use the strengths of each model, which could result in improved accuracies. [PERSON] et al. [41] proposed a multimodal DL model comprising a shallow CNN and a graph convolutional network for wetland classification. Through the fusion of Sentinel-1 and Sentinel-2 data, their proposed model indicated superior performance in comparison to traditional ML and CNN models. Their hybrid model results showed significant promise for large-scale wetland mapping while requiring only a minimal amount of training data. [PERSON] et al. [5] presented a spatial-temporal ensemble DL model, WetNet, which included three distinct submodels of RNNs and CNNs. The model's performance was assessed within a wetland area in Newfoundland, Canada, where it surpassed the capabilities of state-of-the-art CNN and RNN models. CNN model capabilities are limited when capturing long-range spatial dependencies within satellite remote sensing images due to their inherent focus on local features. Transformer architectures, on the other hand, have recently shown great promise in their ability to overcome this restriction by using self-attention mechanisms [42; 43; 44]. With the recent advancements of transformer-based models in natural language processing (NLP), these models have paved their way into the field of wetland classification. Transformer models benefit from generalization capabilities to extract long-range patterns [1; 7]. Several studies demonstrated the adaptability of transformer-based models to wetland classification. For example, [PERSON] et al. [1] trained a GAN model to generate synthetic data and subsequently classified them using a vision transformer (ViT) model to map wetlands across three study areas in New Brunswick, Canada. In another investigation by [PERSON] and [PERSON] [7], the application of the shifted windows transformer (Swin) model for coastal wetland classification, using the integration of Sentinel-1 and Sentinel-2 data, yielded remarkable outcomes. In comparison to traditional CNN models, the Swin transformer model resulted in more than 14% improvement in classification accuracy. Despite the notable potential of transformer models in capturing long-range information, they have limited capabilities in handling neighboring information of remote sensing images. However, by combining transformer and CNN architectures, it is possible to address this shortcoming and harness the potential of both models effectively. As a result, combining the advantages of CNNs and transformers offers the chance to improve the classification accuracy of both individual designs even further. [PERSON] et al. [45] demonstrated the effectiveness of integrating global and local information in hyperspectral image classification using a hybrid convolutional transformer model, where 3D convolutions are embedded into a spatial-spectral dual-branch transformer model. This hybrid approach also led to significant improvements in classification accuracy, underscoring the potential benefits of hybrid CNN-transformer models in other remote sensing applications. [PERSON] et al. [19] introduced the WetMapFormer classifier in this context, which combines CNN and transformer models. This approach involves the utilization of CNNs in initial blocks, followed by a local window attention transformer block. This hybrid model resulted in a higher accuracy for mapping wetland classes, surpassing the performance of well-known CNN and transformer models. Despite these advancements, existing models have notable deficiencies. CNNs, while effective in capturing local features, struggle with long-range spatial dependencies crucial for accurate wetland classification. Transformers, although adept at capturing long-range dependencies, suffer from high computational costs and complexity, particularly when dealing with large spatial dimensions.