Change Detection (CD) is important for natural disaster assessment, urban construction management, ecological monitoring, etc. Nevertheless, the CD models based on the pixel-level classification are highly dependent on the registration accuracy of bi-temporal images. Besides, differences in factors such as imaging sensors and season often result in pseudo-changes in CD maps. To tackle these challenges, we establish a registration-integrated change detection framework called ChangeRD, which can explore spatial transformation relationships between pairs of unaligned images. Specifically, ChangeRD is designed as a multitask network that supervises the learning of the perspective transformation matrix and difference regions between images. The proposed Adaptive Perspective Transformation (APT) module is utilized to enhance spatial consistency of features from different levels of the Siamese network. Furthermore, an Attention-guided Central Difference Convolution (AgCDC) module is proposed to mine the deep differences in bi-temporal features, significantly reducing the pseudo-change noise caused by illumination variations. Extensive experiments on unaligned bi-temporal images have demonstrated that ChangeRD outperforms other SOTA CD methods in terms of qualitative and quantitative evaluation. The code for this work will be available on GitHub.

中文翻译：

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ChangeRD：用于未对齐遥感影像的配准集成变化检测框架


变化检测 （CD） 对于自然灾害评估、城市建设管理、生态监测等非常重要。然而，基于像素级分类的 CD 模型高度依赖于双时间图像的配准精度。此外，成像传感器和季节等因素的差异往往会导致 CD 地图的伪变化。为了应对这些挑战，我们建立了一个名为 ChangeRD 的注册集成变化检测框架，该框架可以探索未对齐图像对之间的空间变换关系。具体来说， ChangeRD 被设计为一个多任务网络，用于监督透视变换矩阵和图像之间差异区域的学习。所提出的自适应透视变换 （APT） 模块用于增强来自孪生网络不同级别的特征的空间一致性。此外，提出了一种注意力引导的中心差分卷积 （AgCDC） 模块来挖掘双时间特征的深层差异，显着降低了由照明变化引起的伪变化噪声。对未对齐的双时相图像的广泛实验表明，ChangeRD 在定性和定量评估方面优于其他 SOTA CD 方法。这项工作的代码将在 GitHub 上提供。