We address the problem of unsupervised visual domain adaptation for transferring scene category models and scene attribute models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a discriminative cross-view subspace alignment algorithm where each view is represented by a subspace spanned by eigenvectors. The source subspace is created using partial least squares correlation, whereas the target subspace is constructed by principal component analysis. Then, a mapping that aligns the source subspace and the target subspace is learned by minimizing a Bregman matrix divergence function. Finally, we project the labeled source data into the target aligned source subspace and the unlabeled target data into the target subspace and perform classification. Experimental results demonstrate that it is possible to use a scene category model or a scene attribute model learned on a set of ground view scenes for classification of VHR remote sensing images. Furthermore, the transferred visual attribute-based representations are human understandable and the classification results are better or comparable with state-of-the-art methods.
['Hao Sun', 'Shuai Liu', 'Shilin Zhou', 'Huanxin Zou']
Unsupervised Cross-View Semantic Transfer for Remote Sensing Image Classification