Abstract
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcome the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at this https URL.
Abstract (translated)
自动人工智能算法和遥感仪的快速发展为变化检测（CD）任务带来了好处。然而，对于精确检测，尤其是在变化特征的边缘完整性和内部孔现象方面，还有很多需要研究的问题。为了解决这些问题，我们设计了一个名为Change Guiding Network（CGNet）的工具，用于解决传统U-Net结构中变化特征表达不足的问题，导致不准确的边缘检测和内部孔。从具有丰富语义信息的深度特征中生成并使用作为先验信息的变体，引导多尺度特征融合，从而提高变化特征的表达能力。同时，我们提出了一个名为Change Guide Module（CGM）的自注意力模块，可以有效地捕捉像素之间的长距离依赖关系，有效克服传统卷积神经网络的接收域不足的问题。在四个主要CD数据集上，我们验证了CGNet的可用性和效率，并通过大量的实验和消融研究证明了CGNet的有效性。我们将开源我们的代码到这个链接：https://github.com/your_username/change_guiding_network。
URL
https://arxiv.org/abs/2404.09179
