Characterizing Variability and Multi-Resolution Predictions of Virtual Sensors

Ashok N. Srivastava, Ph.D. and Rama Nemani, Ph.D.
(Code TI and SG)

In previous papers, we introduced the idea of a Virtual Sensor,
which is a mathematical model trained to learn the potentially
nonlinear relationships between spectra for a given image scene for
the purpose of predicting values of a subset of those spectra when
only partial measurements have been taken. Such models can be
created for a variety of disciplines including the Earth and Space
Sciences as well as engineering domains.  These nonlinear
relationships are induced by the physical characteristics of the
image scene.  In building a Virtual Sensor a key question that
arises is that of characterizing the stability of the model as the
underlying scene changes.  For example, the spectral relationships
could change for a given physical location, due to seasonal weather
conditions.  This talk discusses the stability of predictions
through time and also demonstrates the use of a Virtual Sensor in
making multi-resolution predictions.  In this scenario, a model is
trained to learn the nonlinear relationships between spectra at a
low resolution in order to predict the spectra at a high resolution.