Characterizing Variability and Multi-Resolution Predictions
Shared by Ashok Srivastava, updated on Sep 22, 2010
Summary
- Author(s) :
- Ashok Srivastava, R. Nemani
- 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.
- Publication Name
- Second NASA Data Mining Workshop: Issues and Applications in Earth Science
- Publication Location
- Pasadena, CA
- Year Published
- 2006
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195.7 KB | 44 downloads |
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