液晶与显示2024，Vol.39Issue(10)：1361-1379,19.DOI:10.37188/CJLCD.2024-0086
基于CNN-Transformer结构的遥感影像变化检测
Remote sensing image change detection based on CNN-Transformer structure
摘要
Abstract
Modern high-resolution remote sensing images have achieved remarkable results in change detection with the aid of convolutional neural network(CNN).However,the limited receptive field of convolution operations leads to insufficient learning of global context and long-distance spatial relationships.While visual Transformers effectively capture dependencies in remote features,their handling of details in image changes is insufficient,resulting in limited spatial localization capabilities and low computational efficiency.To address these issues,this paper proposes a multi-level cross-layer linear fusion end-to-end encoding-decoding hybrid CNN-Transformer change detection model based on dilated spatial pyramid pooling,combining the advantages of visual Transformers and CNN.Firstly,image features are extracted using Siamese CNN,refined through dilated pyramid pooling to better capture detailed feature information.Secondly,the extracted attributes are converted into visual words,and a Transformer encoder models the compact visual words,feeding the learned context-rich labels back into visual space through a Transformer decoder to reinforce the original features.Thirdly,CNN features are fused with the features from Transformer encoding-decoding through skip connections,facilitating the fusion of position and semantic information by connecting features of different resolutions through upsampling.Finally,a difference enhancement module generates difference feature maps containing rich change information.Comprehensive experiments conducted on four publicly accessible remote sensing datasets,including LEVIR,CDD,DSIFN and WHUCD,confirm the efficacy of the proposed approach.Compared with other cutting-edge techniques for detecting changes,the model presented in this paper achieves superior classification performance,effectively addressing issues such as under-segmentation,over-segmentation and rough edge segmentation in change detection results.关键词
遥感图像/变化检测/卷积神经网络/Transformer/空间空洞金字塔池化Key words
remote sensing images/change detection/convolutional neural network/transformer/atrous spatial pyramid pooling分类
信息技术与安全科学引用本文复制引用
潘梦洋,杨航,范祥晖..基于CNN-Transformer结构的遥感影像变化检测[J].液晶与显示,2024,39(10):1361-1379,19.基金项目
中国科学院青年创新促进会(No.2020220)Supported by Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2020220) （No.2020220）
