@article{fdi:010086166,
  title = {{A} combined color and wave-based approach to satellite derived bathymetry using deep learning},
  author = {{A}l {N}ajar, {M}. and {E}l {B}ennioui, {Y}. and {T}houmyre, {G}r{\'e}goire and {A}lmar, {R}afael and {B}ergsma, {E}. {W}. {J}. and {B}enshila, {R}. and {D}elvit, {J}. {M}. and {W}ilson, {D}. {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{K}nowledge of the evolution of the littoral zone over time is paramount for coastal science and coastal zone management. {H}owever, traditional bathymetric surveys using echo-sounding techniques are unsuitable for large-scale applications due to a variety of constraints. {O}n the other hand, remote sensing data such as satellite imagery allow for the development and application of bathymetry inversion models on a large scale. {D}eep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various {E}arth {O}bservation and model inversion applications. {I}n this work, we develop and apply a deep learning-based depth inversion model combining wave kinematics and water color information from {S}entinel-2 satellite imagery. {W}e present two different satellite image processing methods to augment wave kinematics and color information as inputs to the proposed deep learning-based models. {W}e show competitive results with a state-of-the-art physical inversion method for satellite derived bathymetry, {S}atellite to {S}hores ({S}2{S}hores), demonstrating a promising direction for the use of deep learning models in {S}atellite {D}erived {B}athymetry ({SDB}) and {E}arth observation in general.},
  keywords = {{GUYANE} {FRANCAISE} ; {ATLANTIQUE}},
  booktitle = {},
  journal = {{T}he {I}nternational {A}rchives of the {P}hotogrammetry, {R}emote {S}ensing and {S}patial {I}nformation {S}ciences},
  volume = {{XLIII}-{B}3-2022},
  numero = {},
  pages = {9--16},
  ISSN = {2194-9034},
  year = {2022},
  DOI = {10.5194/isprs-archives-xliii-b3-2022-9-2022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086166},
}