Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

Journal: Statistics in biosciences
Published Date: Nov 28, 2016

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.

Authors

  • Mark A Wolters
    Shanghai Center for Mathematical Sciences, Fudan University, 22nd Floor, Guanghua Tower East, 220 Handan Road, Shanghai, China 20043.
  • C B Dean
    Department of Statistical and Actuarial Sciences, Western University, Western Science Centre, Room 262, 1151 Richmond Street, London, Ontario, Canada N6A 5B7.

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