{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Ashok Srivastava", "hasEmail": "mailto:ashok.n.srivastava@gmail.com"}, "description": "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.", "distribution": [{"@type": "dcat:Distribution", "description": "Paper", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/JPL2006.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "JPL2006.pdf"}], "identifier": "DASHLINK_156", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/156/", "modified": "2025-04-01", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Characterizing Variability and Multi-Resolution Predictions"}