基于伪全局Swin Transformer的遥感图像识别算法

Remote Sensing Image Recognition Algorithm Based on Pseudo Global Swin Transformer

如何在多目标并列的情况下,确定符合人类思维习惯的核心目标是遥感图像识别的关键之一.因此,在全局视野下,为各目标分配符合人类视觉习惯的注意力,是甄选核心目标的有效途径之一.文中结合Transformer提取全局特征的思想和Swin Transformer对图像栅格化处理可降低计算量的优点,提出基于伪全局Swin Transformer的遥感图像识别算法.构建伪全局Swin Transformer模块,将遥感图像栅格化后的各局部信息聚合为一个特征值,替代以像素为基础的全局信息,以较小计算量为代价,获取全局特征,有效提升模型对所有目标的感知能力.同时,通过以可变形卷积为基础的感受野自适应缩放模块,使感受野向核心目标偏移,提高网络对核心目标信息的关注,从而实现对遥感图像的精确识别.在RSSCN7、AID和OPTIMAL-31遥感图像数据集上的实验表明,文中算法取得较高的识别精度和参数识别效率.

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.