In urban complex scenes,features have diverse shapes,and changes in illumination and imaging angle can cause interference in change detection results.To solve these problems,this paper proposes a dual context multi-scale supervised fusion network model(DCMSFNet).First,the dual context enhancement module is used in the encoding part to obtain the rich global context information of the features.In the decoding part,the cascade approach is used to combine the features,and then the adaptive attention module is used to capture the change relationships at different scales,and the multi-scale supervised fusion module is designed to enhance the deep network fusion and obtain the features of the change region with higher discriminative ability,and the outputs of the different layers are combined with the main network's reconstructed change map fusion to form the final change detection results.The model proposed in this paper achieves better results in the LEVIR-CD and SYSU-CD change detection dataset,with a 1.58％and 2.17％improvement in F1-score,which can more accurately recognize the change regions of complex scenes,further reduce the false detection and omission caused by irrelevant factors,and smoother detection of the edges of the target features.
关键词
深度学习/变化检测/双上下文增强/自适应注意力模块/多尺度监督融合
Key words
deep learning/change detection/dual context enhancement/adaptive attention module/multi-scale supervised fusion
维普
万方数据