Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images

In order to make full use of local neighborhood information for high-resolution remote sensing images, this study combined iterative slow feature analysis (ISFA) and stacked denoising autoencoder (SDAE) to improve the change detection precision. First, this approach introduced ISFA for initial change detection in an unsupervised way, which enlarged the separability of changed and unchanged areas. Then, by setting different membership degrees, the changed and unchanged samples were obtained through fuzzy-means clustering. Finally, the change model was built by SDAE to represent the local neighborhood features deeply, and the change detection result can be obtained after all the samples were fed into the model. Experiments were performed on three real datasets, and the results validated the effectiveness and superiority of the proposed approach.