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

Author

Listed:
  • Mark A. Wolters

    (Fudan University)

  • C. B. Dean

    (Western University)

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.

Suggested Citation

  • Mark A. Wolters & C. B. Dean, 2017. "Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 622-645, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9185-5
    DOI: 10.1007/s12561-016-9185-5
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    References listed on IDEAS

    as
    1. Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    2. Wolters, Mark A., 2015. "A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(c01).
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    Full references (including those not matched with items on IDEAS)

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