Semi-supervised learning of heterogeneous data in remote sensing imagery

Abstract

We analyze Schroedinger Eigenmaps - a new semi-supervised manifold learning and recovery technique - for applications in hyperspectral imagery. This method is based on an implementation of graph Schroedinger operators with appropriately constructed potentials as carriers of expert/labeled information. In this paper, we analyze the features of Schroedinger Eigenmaps through analysis of the potential locations and their imapct on the classication. The imaging modalities which we shall incorporate in our analysis include multispectral and hyperspectral imagery. For the purpose of constructing ecient methods for building the potentials we refer to expert ground-truth data, as well as to using automated clustering techniques. We also investigate the role of dierent sources of the barrier potential locations, and the role they play in the separation of classes.