注意力引导全尺度连接网络的高分辨率影像变化检测

CBAM UNet+++:Attention mechanism to guide change detection studies of full-scale connected networks

针对普通跳跃连接缺乏从全尺度角度获取变化信息及编码器特征提取能力不足的问题,本文设计了耦合注意力机制CBAM(Convolutional Block Attention Module)的UNet+++高分辨率遥感影像变化检测网络CBAM UNet+++。CBAM UNet+++基于融合全尺度特征的语义分割结构UNet+++,同时替换基本卷积单元为残差注意力模块ResBlock_CBAM(Residual Block_CBAM)抑制背景影响,增强编码器对显著特征的学习能力,并利用两种不同变化类型的高分辨率遥感影像变化检测数据集进行验证。结果表明:该方法在LEBEDEV多地物变化数据集上取得最高精度,F1(F1-Score)和OA(Overall Accuarcy)值分别为88.9%、97.3%;在LEVIR-CD建筑物变化数据集上取得次高精度,F1和OA值分别为86.7%、96.8%;同时,该法能有针对性的获取深层语义,定性结果优于其他基准网络。

Existing change detection networks rely heavily on layer-by-layer convolution for feature extraction.However,the use of this method leads to a loss of information,and it lacks the ability to mine important change features.Therefore,knowing how to effectively suppress the influence of the background and identifying ways to increase the ability of the network to learn salient features and generate recognizable feature information are highly important for change detection tasks.Traditional skip connections lack the ability to obtain change information from a full-scale perspective and perform encoder feature extraction.Thus,a UNet+++high-resolution remote sensing image change detection network called CBAM UNet+++combined with a coupled attention mechanism(i.e.,a convolutional block attention module[CBAM])was designed in this research.CBAM UNet+++is based on the semantic segmentation structure UNet+++.The unique full-scale concatenation operation of UNet+++effectively fuses the semantic and spatial information from the full-scale perspective to avoid information loss.The basic convolutional unit can be replaced by a residual attention module(Residual Block_CBAM and ResBlock_CBAM)to suppress background effects and enhance the learning ability of the encoder to handle significant features.The residual attention module was validated on two remote sensing image change detection datasets—LEBEDV and LEVIR-CD—involving different high-resolution change regions.The proposed method has the highest accuracy on the LEBEDEV multifeature change dataset,with F1 and OA values of 88.9%and 97.3%,respectively,and the second highest accuracy on the LEVIR-CD building change dataset,with F1 and OA values of 86.7%and 96.8%,respectively.The proposed method can obtain deep semantics in a targeted manner,and its qualitative results are better than those of other benchmark networks.The CBAM UNet+++method can accurately locate and detect change regions with better detection and accuracy than can the benchmark method.The accuracy results of the two selected datasets were slightly different,but they were not inconsistent.The accuracy of the CBAM UNet+++model was disrupted by pseudochange information in the building dataset.Future work may focus on the usability of this network for change detection in heterogeneous dual-temporal images to further address the impact of early fusion on change detection accuracy.