February 5-7, 2018 | Denver, CO
Taking place with

Bathymetry by Fusion of Airborne Laser Scanning and Multi-spectral Aerial Imagery

07 Feb 2018
8:30 am - 8:55 am

Bathymetry by Fusion of Airborne Laser Scanning and Multi-spectral Aerial Imagery

Modern airborne bathymetric sensors (e.g. Teledyne Optech Titan/CZMIL Nova, Leica Chiroptera II/HawkEye II+III, Riegl VQ-880-G, etc.) incorporate laser scanners and multi-spectral cameras. Measuring shallow water depths with optical remote sensing, however, is mainly conducted independently so far by either exploiting the multi-spectral image content, by applying through-water photogrammetry based either on overlapping multi-view stereo images, or by measuring the travel-time of short green laser pulses in water. Nevertheless, the increasing availability of active and passive remote sensing data acquired simultaneously from the same platform demands for integrated data processing.

The potential benefits of a comprehensive strategy are manifold:

(1) Exploiting the complementary measurement techniques could improve the accuracy, reliability and completeness of Digital Terrain Models of the submerged topography.
(2) Bathymetric lidar operates in the green domain of the visible spectrum. Especially for clear water, certain bands of multi-spectral data may provide better water column penetration (e.g., coastal blue-blue, λ=430-500 nm).
(3) Depths derived from laser bathymetry constitute optimum reference data for calibrating models for spectrally based depth and/or substrate type estimation. This fact can be used to set up automated procedures for processing multi-spectral image data.
(4) The main advantage of laser bathymetry is that the depth is not derived from radiometric information (signal strength) but via time measurement. Knowing the water depth reduces the unknowns for spectrally based techniques which helps to distinguish substrate soil types, benthic habitats, etc. Fusion of passive image data and active laser scans should therefore improve the object classification (sand, gravel, rock, submerged vegetation, etc.).
(5) To achieve better classification results, existing state-of-the-art techniques like Conditional Random Fields, which incorporate contextual information, need to be extended for the use with comprehensive active and passive remote sensing data as input.
(6) Whereas the spatial resolution of laser bathymetry is fundamentally limited by the laser footprint diameter, a much higher resolution in the range of the ground sampling distance of a single image pixel can be achieved with dense image matching (DIM). Embedding DIM in a multimedia-photogrammetry framework could lead to very high-resolution terrain models of the littoral zone.

The outlined topics are currently addressed by analyzing existing bathymetric data of both the coastal zone (German Baltic Sea) and riverine environments (Alpine gravel-bed river) captured with different laser sensors and camera systems. In addition to that, data acquisition of two mountain lakes in the Stubaier Alps was carried out with a topo-bathymetric laser scanner and multi-spectral cameras. We present first promising results of this case study including a comparison against multi-beam sonar data as reference, which confirm that integrated data processing indeed leads to more reliable depth estimates and improved classification of substrate types and benthic habitats.

Track Name: Bathymetric
Session Date: Feb 7 2018 8:30 am – 8:55 am

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