Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNNs) 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.

中文翻译：

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通过基于层注意的抗噪网络进行合成孔径雷达图像变化检测


近年来，基于卷积神经网络（CNN）的合成孔径雷达（SAR）图像变化检测方法受到越来越多的研究关注。然而，现有的基于CNN的方法忽略了多层卷积之间的相互作用，预分类中涉及的错误限制了网络优化。为此，我们提出了一种基于层注意力的耐噪网络，称为 LANTNet。特别是，我们设计了一个层注意模块，自适应地对不同卷积层的特征进行加权。此外，我们设计了一种耐噪声损失函数，可以有效抑制噪声标签的影响。因此，该模型对预分类结果中的噪声标签不敏感。三个 SAR 数据集上的实验结果表明，与几种最先进的方法相比，所提出的 LANTNet 表现更好。源代码可在 https://github.com/summitgao/LANTNet 获取。