Online Program Home
My Program

Abstract Details

Activity Number: 50
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: SSC
Abstract #318398
Title: An Autologistic Regression Model for Binary Classification of Hyperspectral Remote Sensing Imagery
Author(s): Charmaine Dean and Mark Wolters*
Companies: University of Western Ontario and Fudan University
Keywords: machine learning ; hyperspectral images ; image segmentation ; autologistic regression ; forest fire smoke
Abstract:

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.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association