Change detection in remote sensing images based on manifold regularized joint non-negative matrix factorization

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.