论文标题
合成孔径雷达图像图像通过基于注意力的耐噪声网络更改检测
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
论文作者
论文摘要
最近，基于卷积神经网络（CNN）的合成孔径雷达图像（SAR）图像的变更检测方法已越来越多。但是，现有的基于CNN的方法忽略了多层卷积之间的相互作用，而涉及预制的错误限制了网络优化。为此，我们提出了一个基于注意力的噪声网络，称为Lantnet。特别是，我们设计了一个层注意模块，该模块适应了不同卷积层的特征。此外，我们设计了一个耐噪声的损失函数，可有效抑制嘈杂标签的影响。因此，该模型对预制结果中的嘈杂标签不敏感。三个SAR数据集的实验结果表明，与几种最新方法相比，所提出的Lantnet的性能更好。源代码可在https://github.com/summitgao/lantnet上找到
Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function that effectively suppresses the impact of noisy labels. Therefore, the model is insensitive to noisy labels in the preclassification results. The experimental results on three SAR datasets show that the proposed LANTNet performs better compared to several state-of-the-art methods. The source codes are available at https://github.com/summitgao/LANTNet
