A cross-attention integrated shifted window transformer for remote sensing image scene recognition with limited data

The aim of remote sensing image scene recognition is to label a set of semantic categories based on their contents, and recognition for remote sensing images has a wide range of applications in many fields. However, it is a great challenge to extract category features with insufficiently labeled samples. We propose a Multi-scale Shift-window Cross-attention Vision Transformer (MSC-ViT) framework for remote sensing image scene recognition with limited data. Specifically, the proposed model is composed of three modules: a multi-scale feature extraction module, a shift-window transformer module, and a multi-scale cross-attention module. First, to enhance the efficiency of data utilization, we design a multi-scale module to fully extract the features of object information and spatial information contained in the image. The hierarchical transformer structure based on shifted windows, which are flexible at different scales, could match the computation of multi-scale features. The token fusion method based on the cross-attention mechanism fuses the features between multi-branch tokens and class tokens, which fully learn the information of the tokens and achieve better classification results. In addition, we integrate existing open-source datasets of remote sensing images and form a new dataset to better apply to the scene recognition task of remote sensing images with limited data. Our experimental results show that the proposed method achieves a great performance in scene classification of remote sensing images with limited data. The top-1 accuracy of the developed method is 79.84% with a 20% training ratio, 84.78% with a 40% training ratio, 89.79% with a 60% training ratio, and 91.43% with an 80% training ratio.