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

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.

Canadian contributions to environmetrics

“…This requires sophisticated data fusion, processing, and modelling techniques. Research identifying smoke is also important for respiratory health applications (e.g., Wan et al, 2011; Wolters & Dean, 2017).…”