@article{ref1,
title="Classification of large-scale remote sensing images for automatic identification of health hazards: smoke detection using an autologistic regression classifier",
journal="Statistics in biosciences",
year="2017",
author="Wolters, Mark A. and Dean, C. B.",
volume="9",
number="2",
pages="622-645",
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.<p /> <p>Language: en</p>",
language="en",
issn="1867-1764",
doi="10.1007/s12561-016-9185-5",
url="http://dx.doi.org/10.1007/s12561-016-9185-5"
}