In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote sensing images, we propose a new network based on U-Net in this paper. The attention mechanism is cleverly applied in the change detection task, and the data-dependent upsampling (DUpsampling) method is used at the same time, so that the network shows improvement in accuracy, and the calculation amount is greatly reduced. The experimental results show that, in the two-phase images of Yinchuan City, the proposed network has a better antinoise ability and can avoid false detection to a certain extent.

中文翻译：

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基于注意机制的遥感图像变化检测。

近年来，基于卷积神经网络（CNN）的图像处理方法取得了很好的效果。同时，已经提出了许多分支技术来提高准确性。针对遥感图像的变化检测任务，本文提出了一种基于U-Net的新网络。注意机制巧妙地应用于变化检测任务中，同时使用了依赖于数据的上采样（DUpsampling）方法，网络显示出了提高的准确性，并大大减少了计算量。实验结果表明，在银川市两相图像中，该网络具有较好的抗噪能力，可以在一定程度上避免误检。