Unsupervised change detection of remote sensing images based on semi-nonnegative matrix factorization

In this paper, we propose an unsupervised change detection approach for the multitemporal remote sensing images based on semi-nonnegative matrix factorization (semi-NMF). Specifically, the multitemporal source images, acquired at the same geographical area but at two different time instances, are first utilized to generate the difference image. Then, feature vector is created for each pixel of the difference image in such a way that its corresponding h × h block data is projected on the generated eigenvector space by principal component analysis (PCA), which is further arranged as a column vector to form a feature-by-item data matrix X. Next, we implement semi-NMF to factorize X into two nonnegative factors (i.e., the basis matrix F and the coefficient matrix G). Finally, the change detection is achieved by discriminating each column of G^T according to the maximum criterion. Experimental results verify the feasibility and effectiveness of the proposed approach.