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Related Experiment Video

Updated: May 22, 2026

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

Avishai Ben-David¹, Charles E Davidson

¹RDECOM, Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland 21010, USA. avishai.bendavid@us.army.mil

|April 27, 2012

Summary

A new probability model enhances passive infrared remote sensing by analyzing a 3-layer radiative transfer model. This model, using Johnson SU distributions, improves target detection and performance evaluation in noisy conditions.

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Last Updated: May 22, 2026

Scattering And Absorption of Light in Planetary Regoliths

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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

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Published on: August 29, 2019

Area of Science:

Remote Sensing
Radiative Transfer Modeling
Probability Theory

Background:

Passive infrared remote sensing relies on 3-layer radiative transfer models.
Accurate modeling is crucial for target detection and performance evaluation.
Existing models often lack comprehensive statistical treatment of parameters.

Purpose of the Study:

Develop a novel probability model for a 3-layer radiative transfer system.
Incorporate a wide range of statistical distributions to capture model complexities.
Enable robust evaluation of detection capabilities and performance metrics.

Main Methods:

Developed a probability model based on the Johnson family of distributions.
Utilized the Johnson SU distribution to fit theoretically computed moments.
Treated all radiative transfer parameters as random variables, including higher-order statistics.

Main Results:

The probability model accurately fits the moments of the 3-layer radiative transfer model.
The Johnson SU distribution effectively captures varying skewness and kurtosis.
The model allows for evaluation of target detection probability and thermal contrast.

Conclusions:

This work presents the first probability model for 3-layer remote sensing geometry with random variables and higher-order statistics.
The developed model enhances the analysis of passive infrared remote sensing scenarios.
It provides a framework for evaluating performance in clutter-noise limited environments.