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Characterizing Variability and Multi-Resolution Predictions

Metadata Updated: December 7, 2023

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.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Identifier DASHLINK_156
Data First Published 2010-09-22
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 1aa9e290-5623-4f42-b6ff-820bfe28c3f9
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash 40387c64298b159d135bea29e721cc592ecb19b335ada461bba45cc69efc7d4f
Source Schema Version 1.1

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