Remote Sensing Image Recognition Algorithm Based on Pseudo Global Swin Transformer

Determining the core target aligning with human thinking habits in the context of multiple concurrent targets is one of the key factors in remote sensing image recognition. Therefore,the effective allocation of attention in accordance with human visual habits in a global perspective is one of the ways to select core targets. In this paper, combining the concept of extracting features using the Transformer and the advantages of the Swin Transformer in reducing computational complexity through image gridding, a remote sensing image recognition algorithm based on pseudo global Swin Transformer is proposed. The pseudo global Swin Transformer module is built to aggregate the local information of rasterized remote sensing images into a single feature value, replacing the pixel-based global information to obtain global features with smaller computational cost, and thus the perceptual ability of the model for all targets is effectively improved. Meanwhile, by introducing a receptive field adaptive scaling module based on deformable convolutions, the receptive field is shifted towards core targets to enhance the network attention to core target information and then achieve precise recognition of remote sensing images. Experiments on RSSCN7, AID, and OPTIMAL-31 remote sensing image datasets show that the proposed algorithm achieves high recognition accuracy and parameter identification efficiency. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.