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Advancing Multispectral Image-Derived Physics-Based Bathymetry: Multiobjective Evolutionary Computation for Shallow Water Depth Retrieval

Cong Lei,Ruru Deng,Rong Liu,Jiayi Li,Yu Guo,Junying Yang,Zhenqun Hua,Ruiwu Zhang

Published 2025 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ABSTRACT

Satellite derived bathymetry (SDB) has become a cost effective and efficient way to monitor coral reefs on a large scale. Physics-based SDB methods retrieve bathymetry by minimizing a cost function that measures the difference between the observed spectra and radiative transfer model simulations. Most existing physics-based SDB methods adopt a single cost function, typically focus on matching spectral amplitude, and ignore other complementary spectral discriminators such as spectral shape. This limitation can compromise retrieval accuracy when spectral amplitude exhibits significant variability due to noise, varying illumination conditions, or benthic endmember variability. To address this problem, this article formulates physics-based SDB as a constrained multiobjective optimization problem. The decomposition-based constrained multiobjective differential evolution (DCMODE) algorithm is proposed to simultaneously optimize two cost functions, namely the root-mean-square error (RMSE) and the spectral angle distance. In DCMODE, evenly distributed weight vectors are produced to guide the optimization under different tradeoff preferences. The constrained penalty boundary intersection is introduced as a quantitative criterion for evaluating solutions based on convergence, diversity, and feasibility. Bathymetry results from Landsat-8 OLI images in four study areas in the Xisha Islands indicate that DCMODE achieves higher accuracy than single-objective optimization methods, decreasing the RMSE by 0.03–0.12 m and the mean absolute error by 0.16–0.19 m. This article demonstrates that multiobjective optimization, as embodied in the DCMODE algorithm, provides a promising framework for enhancing the robustness of physics-based SDB methods.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Physics, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/JSTARS.2025.3595207
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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