基于多尺度循环注意力网络的遥感影像场景分类方法

Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network

高分辨率遥感影像场景分类一直是遥感领域的研究热点.针对遥感场景对尺度的需求具有多样性的问题,提出了一种基于多尺度循环注意力网络的遥感影像场景分类方法 .首先,通过Resnet50提取遥感影像多个尺度的特征,采用注意力机制得到影像不同尺度下的关注区域,对关注区域进行裁剪和缩放并输入到网络.然后,融合原始影像不同尺度的特征及其关注区域的影像特征,输入到全连接层完成分类预测.此分类方法在UC Merced Land-Use和NWPU-RESISC45公开数据集上进行了验证,平均分类精度较基础模型Resnet50分别提升了1.89%和2.70%.结果表明,多尺度循环注意力网络可以进一步提升遥感影像场景分类的精度.

Scene classification of high-resolution remote sensing images has always been a research hotspot in the field of remote sensing. In view of the diversity of scale requirements of remote sensing scenes, in this paper it proposes a remote sensing image scene classification method based on multi-scale cyclic attention network. Firstly, the features of multiple scales of remote sensing scene image are extracted by Resnet50 network, the attention mechanism is used to obtain the region of interest of the image, and the region of interest is clipped and scaled. Then, the features of different scales of the original image and the features of different scale cropped images are fused, input to the full connection layer for classification prediction. The proposed method is validated in UC Merced Land-Use and NWPU-RESISC45, the average classification accuracy is improved by 1.89% and 2.70% respectively compared with Resnet50.The results show that the multi-scale cyclic attention network can further improve the accuracy of remote sensing image scene classification.