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- W2076932110 abstract "This paper explores an approach for predicting land cover types of central Montana, USA based on satellite measurementand digital terrain data. We assume a non-linear inherent relationship existing between land cover types and Landsat TMreflectance and terrain variables. To measure this relationship we use Generalize Linear Models (GLMs), which aremathematical extensions of ordinary least-square regression models. Specifically, stepwise logistic regression techniqueis applied to optimize the predictive model. For the analysis of the significance of dropping or adding terms, the Akaikeinformation criterion (AIC) is used. Likelihood Ratio Test (LRT) is applied to test the validity of explanative potential ofpredictor variables. We use cross-validation method to evaluate the predicative accuracy of land cover mapping usingGLMs. Finally we table the relative risk ratios of GLMs. Since relative risk ratios explicitly represent the explanativeefficiency of predictor variables, their ranking can pick up the variables with significant explanatory potential indiscriminating land cover types, which will be significative for simplifying the predictive models. It is anticipated thatGLMs will be valuable extension to semi-automatic classification of remotely sensed imagery, and an effective tool forland cover mapping." @default.
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- W2076932110 date "2007-06-10" @default.
- W2076932110 modified "2023-09-23" @default.
- W2076932110 title "Generalized linear models for mapping land cover using satellite measurement and digital terrain data" @default.
- W2076932110 doi "https://doi.org/10.1117/12.759481" @default.
- W2076932110 hasPublicationYear "2007" @default.
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