Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards

Mark A. Wolters () and C. B. Dean ()
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Mark A. Wolters: Fudan University
C. B. Dean: Western University

Statistics in Biosciences, 2017, vol. 9, issue 2, No 17, 622-645

Abstract: Abstract Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard 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 datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

Keywords: Machine learning; Hyperspectral images; Image segmentation; Autologistic regression; Forest fire smoke (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9185-5

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