Thumbnail
Access Restriction
Open

Author Srivastava, Ashok N. ♦ Nemani, Rama
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Abstract. 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 paper, based on a talk given at the American Geophysical Union (2005), 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. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article