A novel and effective change detection method based on manifold regularized joint non-negative matrix factorization (MJNMF) framework is proposed in this paper, which detects the changes that occurred in multi-temporal remote sensing images. Most change detection methods, including dictionary learning, principal component analysis (PCA), etc., do not consider the non-negativity among image pixels. However, image itself is a non-negative signal, and the non-negative constraint has better interpretability in practical applications. Nonnegative Matrix Factorization, which incorporates the non-negativity constraint and thus learns object parts, obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. In this paper, our proposed approach based on MJNMF framework aims to establish a pair of joint basis matrices by unchanged training samples from unchanged area. Then, unchanged pixels can be well reconstructed by the corresponding basis matrix, while changed pixels cannot be reconstructed from the basis matrix corresponding to the knowledge of unchanged samples, or a larger reconstruction error can be generated even if changed pixels are reconfigurable. In order to suppress similar information and highlight different information, the cross-reconstruction error is used to generate the difference image. Finally, the binary image is obtained by the robust fuzzy local information c-means (FLICM) clustering algorithm. In addition, inspired by manifold learning, we incorporate manifold regularization into the proposed method to keep the geometric structure of data and improve the accuracy of change detection. Experimental results obtained on simulated and real remote sensing images confirm the effectiveness of the proposed method.

提出了一种基于流形正则联合非负矩阵分解（MJNMF）框架的新颖有效的变化检测方法，该方法可以检测多时相遥感影像中发生的变化。大多数变化检测方法，包括字典学习，主成分分析（PCA）等，都没有考虑图像像素之间的非负性。但是，图像本身是非负信号，并且在实际应用中非负约束具有更好的可解释性。非负矩阵因式分解结合了非负约束，从而学习了对象的零件，从而获得了基于零件的表示形式，并相应地增强了问题的可解释性。在本文中，我们提出的基于MJNMF框架的方法旨在通过来自不变区域的不变训练样本建立一对联合基础矩阵。然后，可以通过相应的基本矩阵很好地重构未改变的像素，而不能根据与未改变的样本的知识相对应的基础矩阵来重构改变的像素，或者即使改变的像素是可重构的，也可能产生更大的重构误差。为了抑制相似信息并突出显示不同信息，交叉重构误差用于生成差异图像。最后，通过鲁棒的模糊局部信息c均值（FLICM）聚类算法获得二值图像。此外，受多种学习启发，我们将流形正则化合并到所提出的方法中，以保持数据的几何结构并提高变更检测的准确性。在模拟和真实遥感图像上获得的实验结果证实了该方法的有效性。