DEEP SEMI-NONNEGATIVE MATRIX FACTORIZATION BASED UNSUPERVISED CHANGE DETECTION OF REMOTE SENSING IMAGES

In the paper, an unsupervised change detection method for remote sensing (RS) images based on deep semi-nonnegative matrix factorization (semi-NMF) is proposed. Firstly, the difference image is generated in different ways, depending on the types of input images. Then principal component analysis (PCA) is applied on the difference image to form the feature matrix X for improving the capability against various noise. In order to exploit more useful information from the resulting feature matrix, deep semi-NMF is introduced to factorize X into L + 1 factors consisting of L nonrestricted matrices {F-l}(l=1)(L) and nonnegative cluster indicator matrix G(L). Finally, the binary change mask (CM) is generated by assigning the pixels into changed and unchanged classes according to maximum criterion. The experimental results on two pairs of multitemporal RS images demonstrate the effectiveness of the proposed method.