Semi-supervised learning of heterogeneous data in remote sensing imagery

J. Benedetto; W. Czaja; J. Dobrosotskaya; T. Doster; K. Duke; D. Gillis

doi:10.1117/12.919259

10 May 2012 Semi-supervised learning of heterogeneous data in remote sensing imagery

J. Benedetto, W. Czaja, J. Dobrosotskaya, T. Doster, K. Duke, D. Gillis

Proceedings Volume 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X; 840104 (2012) https://doi.org/10.1117/12.919259
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

J. Benedetto, W. Czaja, J. Dobrosotskaya, T. Doster, K. Duke, and D. Gillis "Semi-supervised learning of heterogeneous data in remote sensing imagery", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 840104 (10 May 2012); https://doi.org/10.1117/12.919259

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