Improving Extraction of Bathymetry from Lidar Using Machine Learning

TitleImproving Extraction of Bathymetry from Lidar Using Machine Learning
Publication TypeConference Abstract
Year2019
AuthorsLowell, K, Calder, BR
Conference Name20th Annual Coastal Mapping & Charting Workshop of the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX)
Conference LocationNotre Dame, IN
Conference DatesJune 4-6
KeywordsLidar Data Processing, Machine Learning

Three ML techniques – artificial neural networks (ANNs), extreme gradient boosting (XGB), and regularized logistic regression (RLR) – have been applied to lidar pulse return meta-data to estimate the probability that each return is bathymetry -- p(Bath) -- for four tiles from a NOAA Remote Sensing Division shallow-water data in the Florida Keys. To facilitate operationalization, the meta-data are extracted from the point cloud alone – i.e., no ancillary data are employed. Three types of meta-data are being explored: return-specific, ocean floor topography, and flight path crenularity.

Major conclusions to date are:

  • ANNs provide better p(Bath) estimates than XGB and RLR, but provide the least amount of information about what meta-data are most important for prediction.
  • The p(Bath) signal strength as measured by the true positive rate for bathymetry varies considerably across the four tiled data sets – i.e., from 11% to 92% for ANN models.

Ultimately, the p(Bath) estimate will be integrated into an already-operational lidar-extraction algorithm.

 

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