Change detection (CD) aims to identify changes that occur in an image pair taken different times. Prior methods devise specific networks from scratch to predict change masks in pixel-level, and struggle with general segmentation problems. In this paper, we propose a new paradigm that reduces CD to semantic segmentation which means tailoring an existing and powerful semantic segmentation network to solve CD. This new paradigm conveniently enjoys the mainstream semantic segmentation techniques to deal with general segmentation problems in CD. Hence we can concentrate on studying how to detect changes. We propose a novel and importance insight that different change types exist in CD and they should be learned separately. Based on it, we devise a module named MTF to extract the change information and fuse temporal features. MTF enjoys high interpretability and reveals the essential characteristic of CD. And most segmentation networks can be adapted to solve the CD problems with our MTF module. Finally, we propose C-3PO, a network to detect changes at pixel-level. C-3PO achieves state-of-the-art performance without bells and whistles. It is simple but effective and can be considered as a new baseline in this field. Our code will be available.
翻译:变化检测(CD) 旨在识别不同时间图像配对中发生的变化。 先前的方法设计了特定的网络, 从零开始预测像素层面的面罩变化, 并和普通分解问题作斗争。 在本文件中, 我们提出一个新的模式, 将CD降为语义分解, 这意味着调整现有和强大的语义分解网络以解决CD 。 这个新模式方便地享受主流语义分解技术, 以处理CD 中普通分解问题。 因此我们可以集中研究如何检测变化。 我们提出了一个新颖而重要的洞察方法, 即CD 中存在不同的变化类型, 并且应当分别学习。 基于这个方法, 我们设计了一个名为 MTF 的模块, 以提取变化信息并激活时间特性 。 MTF 具有很高的可解释性, 并揭示了 CD 的基本特性 。 多数 分解网络可以被调整, 以解决我们的 MTF 模块的 CD 问题 。 最后, 我们建议 C-3PO, 一个在像素级一级检测变化的网络 。 C-3PO 将实现无钟和哨孔的状态状态性表现。 它简单但有效, 并且可以被视为我们这个字段的新基线 。