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.
Highlights
In the current Big Data era, one may be inclined to believe that relevant data are copious and cheaply available regardless of the circumstances
We summarize previous work that has been done with these data, and describe the autologistic regression model as a natural extension of that work
We have considered improvements to the structure of our logistic regression model, and have incorporated spatial association among adjacent pixels by moving to an autologistic regression model
Summary
In the current Big Data era, one may (naively) be inclined to believe that relevant data are copious and cheaply available regardless of the circumstances. Stat Biosci (2017) 9:622–645 and precision, and measured representatively over the population in question—is not always easy One situation of this type is the assessment of exposure to airborne environmental health hazards. It is a true-colour image constructed from the hyperspectral data (following the guidance in Gumley et al [9]). This particular image is a background image composed by combining the clear-sky portions of 17 individual images and taking the median values of each spectral component at each pixel.
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