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Neural network uncertainty assessment using Bayesian statistics: a remote sensing application.

F Aires1, C Prigent, W B Rossow

  • 1Department of Applied Physics and Applied Mathematics, Columbia University, NASA Goddard Institute for Space Studies, New York, NY 10025, USA. faires@giss.nasa.gov

Neural Computation
|October 13, 2004
PubMed
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This study introduces a Bayesian technique for neural network (NN) uncertainty estimation in remote sensing. It quantifies parameter and output uncertainties, enhancing model robustness and physical interpretability for applications like temperature retrieval.

Area of Science:

  • Remote Sensing
  • Machine Learning
  • Geophysics

Background:

  • Neural networks (NNs) are effective for regression in remote sensing but often lack uncertainty estimates.
  • Traditional NN approaches use point estimation, limiting model robustness assessment.
  • Understanding NN internal structure and dependencies is crucial for reliable, physically coherent models.

Purpose of the Study:

  • To present a Bayesian technique for evaluating uncertainties in NN parameters (synaptic weights).
  • To demonstrate the computation of output uncertainties (error bars, error correlations) from weight uncertainties.
  • To analyze uncertainties in NN Jacobians for assessing model internal structure and physical meaning.

Main Methods:

  • Bayesian inference to estimate uncertainties of NN weights.

Related Experiment Videos

  • Analytical computation of output uncertainties based on weight uncertainties.
  • Monte Carlo integration to assess NN Jacobian robustness.
  • Principal component analysis-based regularization for Jacobian stabilization.

    • Main Results:

    • Demonstrated application to retrieve surface skin temperature, microwave emissivities, and water vapor content using satellite data.
    • Weight uncertainty estimates enable monitoring of NN model robustness.
    • Jacobian analysis reveals model dependency structures, improving physical interpretability.

    Conclusions:

    • The proposed Bayesian approach provides crucial uncertainty estimates for NN parameters and outputs in remote sensing.
    • Quantifying Jacobian uncertainties enhances the reliability and physical interpretability of NN regression models.
    • This method moves beyond black-box models towards robust, physically meaningful applications.