Abstract: Aiming at the problems of under segmentation or over segmentation and rough edge segmentation in the process of urban construction land change detection using traditional semantic segmentation network,this paper proposes a high-resolution remote sensing image change detection method based on twin attention network.In the coding part,twin neural network is used for feature acquisition to retain more image features of different phases.In the deep coding stage,the hole convolution feature pyramid is introduced to realize the extraction and fusion of multi-scale features and increase the receptive field of the network.In the decoding part,the attention mechanism CBAM is used to highlight the useful features and enhance the useful information to improve the accuracy of edge segmentation.Finally,experiment is carried out on the data set of land use change in Loudi City.Experiment shows that the accuracy rate of this method is 92.56%,the accuracy rate is 89.15%,the recall rate is 85.61%,the IOU is 77.53%,the Miou is 83.76%,the F1 score is 87.34%,and the kappa coefficient is 31.42% on the land use change detection data set of Loudi city.The performance index is better than FCN network,u-net network and CBAM u-net network.Experimental results show that this method can effectively solve the problems of under segmentation or over segmentation of change detection results and rough edge segmentation.

Key words: Remote sensing image, Change detection, Attention network, Hole convolution feature pyramid, Twin network