Abstract Details
Activity Number:
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146
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Type:
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Invited
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Date/Time:
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Monday, August 4, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #310807
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View Presentation
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Title:
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Data-Driven Approaches to Detection in Complex Spectral Imagery
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Author(s):
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David Messinger*+
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Companies:
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Rochester Institute of Technology
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Keywords:
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image analysis ;
spectral imagery ;
multispectral ;
hyperspectral
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Abstract:
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Quantitative information extraction from remotely sensed spectral imagery requires models for the "background" and "foreground" image signatures. Given these models, one can then make decisions per-pixel as to the likelihood of the presence of a signature of interest. Traditional processing schemes rely on statistical or linear subspace models and have been successful in several applications. However, the new generation of sensors has a significant improvement in spatial and spectral coverage and resolution: the Worldview-2 sensor has spatial resolution of ~2m in 8 spectral bands in the visible - near infrared spectrum, while airborne hyperspectral imagery has similar spatial resolution with ~200 contiguous, narrow spectral channels in the visible - short wave infrared. These sensors image the Earth's surface at ever-greater levels of detail making it simple to show that assumptions of normality, or that the data are well-defined by linear subspaces, are not well-met. We will describe new data-driven algorithms for applications such as spectral clustering and detection. Results will be shown for space-based multispectral imagery as well as airborne hyperspectral imagery.
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Authors who are presenting talks have a * after their name.
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