Remote Sensing Image Change Detection Based on Difference Enhancement and Dual Attention Transformer
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.
国家科技期刊平台
NSTL
万方数据
维普
