A Novel Graph-Matching-Based Approach for Domain Adaptation in Classification of Remote Sensing Image Pair
This paper addresses the problem of land-cover classification of remotely sensed image pairs in the context of domain adaptation. The primary assumption of the proposed method is that the training data are available only for one of the images (source domain), whereas for the other image (target domain), no labeled data are available. No assumption is made here on the number and the statistical properties of the land-cover classes that, in turn, may vary from one domain to the other. The only constraint is that at least one land-cover class is shared by the two domains. Under these assumptions, a novel graph theoretic cross-domain cluster mapping algorithm is proposed to detect efficiently the set of land-cover classes which are common to both domains as well as the additional or missing classes in the target domain image. An interdomain graph is introduced, which contains all of the class information of both images, and subsequently, an efficient subgraph-matching algorithm is proposed to highlight the changes between them. The proposed cluster mapping algorithm initially clusters the target domain data into an optimal number of groups given the available source domain training samples. To this end, a method based on information theory and a kernel-based clustering algorithm is proposed. Considering the fact that the spectral signature of land-cover classes may overlap significantly, a postprocessing step is applied to refine the classification map produced by the clustering algorithm. Two multispectral data sets with medium and very high geometrical resolution and one hyperspectral data set are considered to evaluate the robustness of the proposed technique. Two of the data sets consist of multitemporal image pairs, while the remaining one contains images of spatially disjoint geographical areas. The experiments confirm the effectiveness of the proposed framework in different complex scenarios.