Activity Number:
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50
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
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Sponsor:
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SSC
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Abstract #318398
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Title:
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An Autologistic Regression Model for Binary Classification of Hyperspectral Remote Sensing Imagery
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Author(s):
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Charmaine Dean and Mark Wolters*
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Companies:
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University of Western Ontario and Fudan University
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Keywords:
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machine learning ;
hyperspectral images ;
image segmentation ;
autologistic regression ;
forest fire smoke
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Abstract:
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Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and non-hazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional data sets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to identification of forest fire smoke.
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Authors who are presenting talks have a * after their name.