Domain Adaptation in the Absence of Source Domain Labeled Samples-A Coclustering-Based Approach

We propose a novel coclustering-based domainadaptation algorithm for simultaneously generating classification maps for a set of remote sensing (RS) multitemporal images in this letter. Unsupervised domain-adaptation techniques consider two different but related domains: a source domain with ample number of labeled samples and a target domain with no labeled data. The task at hand is to build an inference model exploring the available data that is expected to work consistently well in both the domains. This is a challenging problem, since the probability distributions governing both the domains are substantially different leading to the violation of the probably approximate correct assumptions of statistical learning theory. We consider an even complex scenario in this letter by assuming the absence of source domain training samples in the learning process. Our algorithm broadly consists of two stages: first, data from both the domains are projected into a common subspace using geodesic flow kernel in a Grassmannian manifold, and we further propose an iterative coclustering technique to obtain the consistent clustering outcomes for both the domains in the newly defined space. In line with the traditional domain-adaptation approaches, we also consider that both the domains contain the same set of semantic land-cover classes. The proposed method is simple, scalable, and results in highly precise clustering outputs for standard RS data sets.