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Gradient-Based Automatic Lookup Table Generator for Radiative Transfer Models.

Jorge Vicent Servera1, Luis Alonso1, Luca Martino1

  • 1Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain.

IEEE Transactions on Geoscience and Remote Sensing : a Publication of the IEEE Geoscience and Remote Sensing Society
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces GALGA, an algorithm that optimizes radiative transfer model (RTM) lookup tables (LUTs). GALGA reduces LUT size, computation time, and interpolation errors for Earth observation applications.

Keywords:
Atmospheric correctionforward modelinginterpolationlookup table (LUT)radiative transfer models (RTMs).

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Area of Science:

  • Earth observation
  • Atmospheric science
  • Remote sensing

Background:

  • Physically based radiative transfer models (RTMs) are crucial for Earth observation but are computationally intensive.
  • Precomputing lookup tables (LUTs) is common but can lead to large sizes and interpolation errors.

Purpose of the Study:

  • To develop a method for automatically selecting optimal input-output points (nodes) for RTM LUTs.
  • To reduce RTM computation burden and interpolation errors.

Main Methods:

  • Developed the gradient-based automatic LUT generator algorithm (GALGA).
  • GALGA uses an acquisition function incorporating RTM Jacobian evaluation and node distribution information.
  • Applied GALGA to construct and optimize MODTRAN-based LUTs.

Main Results:

  • GALGA reduced LUT size by over 24% compared to pseudorandom distribution.
  • Computation time decreased by 27% with GALGA.
  • Maximum interpolation relative errors were reduced by at least 10%.

Conclusions:

  • GALGA offers an efficient method for automatic LUT design.
  • The proposed methodology can significantly reduce errors and computation time for RTMs.