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Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling

Bag om Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling

Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling is both a research and practice-based reference that bridges the gap between the remote sensing field and the dynamic oceanography exploration field. In this perspective, the book explicates how to apply techniques in synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for oceanic turbulence and front simulation and modeling. It includes detailed algorithms to enable readers to better understand and implement practices covered in their own work and apply QInSAR to their own research. This multidisciplinary reference is useful for researchers and academics in dynamic oceanography and modeling, remote sensing and aquatic science, as well as geographers, geophysicists, and environmental engineers.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780443191558
  • Indbinding:
  • Paperback
  • Sideantal:
  • 250
  • Udgivet:
  • 1. juni 2024
  Gratis fragt
Leveringstid: 8-11 hverdage
Forventet levering: 25. januar 2025

Beskrivelse af Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling

Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling is both a research and practice-based reference that bridges the gap between the remote sensing field and the dynamic oceanography exploration field. In this perspective, the book explicates how to apply techniques in synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for oceanic turbulence and front simulation and modeling. It includes detailed algorithms to enable readers to better understand and implement practices covered in their own work and apply QInSAR to their own research. This multidisciplinary reference is useful for researchers and academics in dynamic oceanography and modeling, remote sensing and aquatic science, as well as geographers, geophysicists, and environmental engineers.

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