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Computationally efficient variable resolution depth estimation



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  • Journal Title:
    Computers & Geosciences
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    A new algorithm for data-adaptive, large-scale, computationally efficient estimation of bathymetry is proposed. The algorithm uses a first pass over the observations to construct a spatially varying estimate of data density, which is then used to predict achievable estimate sample spacing for robust depth estimation across the area of interest. A low-resolution estimate of depth is also constructed during the first pass as a guide for further work. A piecewise-regular grid is then constructed following the sample spacing estimates, and accurate depth is finally estimated using the composite refined grid and an extended and re-implemented version of the cube algorithm. Resource-efficient data structures allow for the algorithm to operate over large areas and large datasets without excessive compute resources; modular design allows for more complex spatial representations to be included if required. The proposed system is demonstrated on a pair of hydrographic datasets, illustrating the adaptation of the algorithm to different depth- and sensor-driven data densities. Although the algorithm was designed for bathymetric estimation, it could be readily used on other two dimensional scalar fields where variable data density is a driver.
  • Keywords:
  • Source:
    Computers & Geosciences, 106: 49-59
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    CHORUS
  • Main Document Checksum:
    urn:sha256:ec49fc7e670056c0eb1ceef3e284bb6c663676180bdf62d81d38439aece2ace4
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  • File Type:
    Filetype[PDF - 14.33 MB ]
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