IEEE Trans Neural Netw Learn Syst. 2020 Mar;31(3):876-890. doi: 10.1109/TNNLS.2019.2910571. Epub 2019 May 16.
Image change detection detects the regions of change in multiple images of the same scene taken at different times, which plays a crucial role in many applications. The two most popular image change detection techniques are as follows: pixel-based methods heavily rely on accurate image coregistration while object-based approaches can tolerate coregistration errors to some extent but are sensitive to image segmentation or classification errors. To address these issues, we propose an unsupervised image change detection approach based on a novel bipartite differential neural network (BDNN). The BDNN is a deep neural network with two input ends, which can extract the holistic features from the unchanged regions in the two input images, where two learnable change disguise maps (CDMs) are used to disguise the changed regions in the two input images, respectively, and thus demarcate the unchanged regions therein. The network parameters and CDMs will be learned by optimizing an objective function, which combines a loss function defined as the likelihood of the given input image pair over all possible input image pairs and two constraints imposed on CDMs. Compared with the pixel-based and object-based techniques, the BDNN is less sensitive to inaccurate image coregistration and does not involve image segmentation or classification. In fact, it can even skip over coregistration if the degree of transformation (due to the different view angles and/or positions of the camera) between the two input images is not that large. We compare the proposed approach with several state-of-the-art image change detection methods on various homogeneous and heterogeneous image pairs with and without coregistration. The results demonstrate the superiority of the proposed approach.
图像变化检测检测同一场景在不同时间拍摄的多幅图像中的变化区域，这在许多应用中起着至关重要的作用。两种最流行的图像变化检测技术如下：基于像素的方法严重依赖于精确的图像配准，而基于目标的方法可以在一定程度上容忍配准误差，但对图像分割或分类误差很敏感。为了解决这些问题，我们提出了一种基于新型二分图差分神经网络（BDNN）的无监督图像变化检测方法。BDNN 是一种具有两个输入端的深度神经网络，可以从两个输入图像中的未变化区域中提取整体特征，其中使用两个可学习的变化伪装图（CDM）分别伪装两个输入图像中的变化区域，从而标记其中的未变化区域。网络参数和 CDM 将通过优化一个目标函数来学习，该目标函数结合了一个损失函数，该损失函数定义为给定输入图像对在所有可能的输入图像对上的似然度，以及对 CDM 施加的两个约束。与基于像素的和基于目标的技术相比，BDNN 对不准确的图像配准不敏感，并且不涉及图像分割或分类。实际上，如果两个输入图像之间的变换程度（由于摄像机的不同视角和/或位置）不是很大，则它甚至可以跳过配准。我们在具有和不具有配准的各种同质和异质图像对上，将所提出的方法与几种最先进的图像变化检测方法进行了比较。结果表明了所提出方法的优越性。
