Neighbor Consistency Baced Unsupervised Manifold Alignment for Classification of Remote Sensing Image

Abstract

We perform unsupervised domain adaptation for classification of remote sensing images by exploiting unsupervised manifold alignment approach. Manifold alignment method utilized corresponding points between domains to align data manifolds of source and target domains, where the corresponding points can be constructed by labeled information. Supposing labeled samples are not available in target domain, we proposed neighbor consistency (NC) constraint to select some target points that have reliable predictions. These points and labeled source data are then used to construct corresponding relationships, resulting in unsupervised manifold alignment. The neighbor consistency based unsupervised manifold alignment is denoted as NCUMA in this paper. Both multispectral and hyperspectral remote sensing data have been used to demonstrate the effectiveness of the NCUMA approach.