Unsupervised Manifold Alignment for Cross-Domain Classification of Remote Sensing Images

Abstract

The original manifold alignment (MA) approach is for semisupervised domain adaptation. Since the target prior information is difficult to obtain, we conduct it in an unsupervised manner, resulting in an unsupervised MA (UMA) method. This approach utilizes the probabilistic prediction results of target data to construct the cross-domain similarity matrix, which characterizes the relationships between domains and is used for alignment. Due to the spectral drift, the prediction results may not be accurate, and thus affect the alignment. We employed spatial filtering and overall centroid alignment method as two preprocessing strategies to improve the prediction results. Furthermore, per-class maximum mean discrepancy (MMD) constraint is introduced to the UMA to further improve the alignment performance. The proposed UMA_MMD algorithm is applied for the classification of remote sensing images, and the experimental results using hyperion multitemporal remote sensing images demonstrated the effectiveness of the proposed approach.