Abstract: Aiming at the problems of different scales of ground objects,insufficient context information and difficult recovery of edge details in remote sensing images,a pixel-level change detection network(PixelNet) based on multi-scale dual self-attention is proposed to realize the task of remote sensing images change detection.On the one hand,the multi-scale feature pyramid based on hybrid cavity convolution is used to extract the convolution features,and the dual self-attention module is added to obtain the channel and spatial attention.The feature receptive field is increased while considering the details and semantic information,and the global context information is further increased.On the other hand,in order to optimize the boundary smoothing fuzzy problem of ground object,a new edge repair module is implemented through automatic joint training of edge sensing loss and weighted contrast loss.To solve the problem of sample imbalance,a data processing strategy of weighted balanced sampling with threshold is proposed to reduce the skew problem of network training caused by the number of changed pixels is much smaller than that of unchanged pixels.Experiments on remote sensing image datasets CDD and LEVIR-CD show that the proposed PixelNet network outperforms SOTA in terms of subjective visual effects and objective evaluation indexes on remote sensing change detection tasks.The detection accuracy is 98.0% and F1 score is 96.7% on the CDD dataset,the accuracy reaches 95.8% and F1 score reaches 87.2% on the LEVIR-CD dataset.It effectively solves the problems of sample imbalance,lack of context information of biphasic features,and classification error of difficult edge examples in remote sensing change detection.

Key words: Change detection, Remote sensing images, Hybrid cavity convolution, Double self-attention, Edge repair