Remote Sensing (Aug 2023)

Improving Shallow Water Bathymetry Inversion through Nonlinear Transformation and Deep Convolutional Neural Networks

  • Shuting Sun,
  • Yifu Chen,
  • Lin Mu,
  • Yuan Le,
  • Huihui Zhao

DOI
https://doi.org/10.3390/rs15174247
Journal volume & issue
Vol. 15, no. 17
p. 4247

Abstract

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Nearshore bathymetry plays an essential role in various applications, and satellite-derived bathymetry (SDB) presents a promising approach due to its extensive coverage and comprehensive bathymetric map production capabilities. Nevertheless, existing retrieval techniques, encompassing physics-based and pixel-based statistical methodologies such as support vector regression (SVR), band ratio, and Kriging regression, exhibit limitations stemming from the intricate water reflectance process and the under-exploitation of the spatial component inherent in SDB. To surmount these obstacles, we introduce employment of deep convolutional networks (DCNs) for SDB in this study. We assembled multiple scenes utilizing networks with varying scale emphasis and an assortment of satellite datasets characterized by distinct spatial and spectral resolutions. Our findings reveal that these deep learning models yield high-caliber bathymetry outcomes, with nonlinear normalization further mitigating residuals in shallow water regions and substantially enhancing retrieval performance. A comparative analysis with the prevalent SVR technique substantiates the efficacy of the proposed methodology.

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