Citation

Wolters MA, Dean CB. Stat. Biosci. 2017; 9(2): 622-645.

Affiliation

Department of Statistical and Actuarial Sciences, Western University, Western Science Centre, Room 262, 1151 Richmond Street, London, Ontario, Canada N6A 5B7.

Copyright

(Copyright © 2017, International Chinese Statistical Association, Publisher Springer Science+Business Media)

DOI

10.1007/s12561-016-9185-5

PMID

29225715

PMCID

PMC5711969

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.

Language: en

Keywords

Autologistic regression; Forest fire smoke; Hyperspectral images; Image segmentation; Machine learning