无线电工程2024，Vol.54Issue(1)：230-238,9.DOI:10.3969/j.issn.1003-3106.2024.01.030
基于差异增强和双注意力Transformer的遥感图像变化检测
Remote Sensing Image Change Detection Based on Difference Enhancement and Dual Attention Transformer
摘要
Abstract
Due to the complexity of objects in the remote sensing scene,illumination changes and registration errors will affect the changes of the object in two images taken at different time.Exploring the relationship between different pixels and convolutional neural networks with more powerful recognition capabilities can improve bi-temporal performance of change detection in remote sensing images.A novel Transformer neural network model is proposed based on differential enhancement and multi-attention mechanism.The ResNeXt unit is introduced into the feature extraction part of the siamese network architecture to improve the accuracy without increasing the complexity of parameters.The transformer encoder-decoder with hierarchical structure is combined with channel and spatial dual attention modules to obtain a larger receptive field and stronger context shaping ability.In addition,the network also pays attention to the differentiated features of bi-temporal images,weights each pixel by introducing a difference enhancement module,selectively aggregates the features,and finally generates a high-precision remote sensing image change feature map.Through experiments on the change detection benchmark dataset LEVIR-CD and DSIFN,it's shown that the detection effect of different buildings,roads and vegetation changes is greatly improved.Compared with existing models,this method is better than the best results in F1,IoU and OA.关键词
遥感图像/变化检测/Transformer/双注意力机制/差异增强Key words
remote sensing image/change detection/Transformer/dual attention mechanism/difference enhancement分类
信息技术与安全科学引用本文复制引用
张青月,赵杰..基于差异增强和双注意力Transformer的遥感图像变化检测[J].无线电工程,2024,54(1):230-238,9.基金项目
国家重点研发计划资助(2018YFC0910700) （2018YFC0910700）
国家自然科学基金(81801778)National Key R&D Program of China(2018YFC0910700) （81801778）
National Natural Science Foundation of China(81801778) （81801778）
