Predicted bathymetric lidar performance of Coastal Zone Mapping and Imaging Lidar (CZMIL)

Ramnath, Vinod; Feygels, Viktor; Kopilevich, Yuri; Park, Joong Yong; Tuell, Grady

doi:10.1117/12.851978

Cited by 13 publications

(10 citation statements)

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“…This pre-analysis classification is important because bathymetric and topographic lidar waveforms have different characteristics and are dealt with differently 3 In the figure, we show cases of clean targets and multiple targets. Clean targets arise from bare land, large roof tops and walls of buildings etc.…”

Section: Algorithm Development: Peak Detectionmentioning

confidence: 98%

“…By computing ranges from the simulated waveforms, we can generate a direct comparison to ranges generated in the simulator. We made every effort to use realistic (even pessimistic) simulated CZMIL waveforms 3 . Therefore, we believe the ranging procedure discussed and the accuracies determined in the simulation are representative of what we expect in actual CZMIL data (when it comes available).…”

Section: Experiments With Simulated Datamentioning

confidence: 99%

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Predicted lidar ranging accuracy for CZMIL

et al. 2010

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Range measurements in CZMIL 1,2 are accomplished with signal processing techniques applied to green lidar waveforms. In the design phase of the project, we developed software to simulate waveforms for CZMIL, and have used these simulated waveforms to design ranging algorithms, and test their accuracies. Our results indicate the topographic ranging accuracy to hard targets should be on the order of 2cm. In this paper, we discuss the simulations, algorithms, and results.

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Section: Algorithm Development: Peak Detectionmentioning

confidence: 98%

Section: Experiments With Simulated Datamentioning

confidence: 99%

Predicted lidar ranging accuracy for CZMIL

et al. 2010

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“…Then, using the position and orientation data for the aircraft, and the pointing information from the lidar, we compute 3D ellipsoid coordinates on the land/sea surface and the seafloor. The resulting products are the sea floor lidar point cloud in ellipsoid heights and the land/sea surface point cloud in ellipsoid heights (h s and h b ) [3][4] . The water depth, D, is then computed as the difference between the two surfaces defined by the point clouds.…”

Section: Level 2 Processor: Flightline-based Auto-processor and Auto-mentioning

confidence: 99%

Conceptual design of the CZMIL data processing system (DPS): algorithms and software for fusing lidar, hyperspectral data, and digital images

Park

Tuell

2010

SPIE Proceedings

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The Data Processing System (DPS) of the Coastal Zone Mapping and Imaging Lidar (CZMIL) has been designed to automatically produce a number of novel environmental products through the fusion of Lidar, spectrometer, and camera data in a single software package. These new products significantly transcend use of the system as a bathymeter, and support use of CZMIL as a complete coastal and benthic mapping tool. The DPS provides a spinning globe capability for accessing data files; automated generation of combined topographic and bathymetric point clouds; a fully-integrated manual editor and data analysis tool; automated generation of orthophoto mosaics; automated generation of reflectance data cubes from the imaging spectrometer; a coupled air-ocean spectral optimization model producing images of chlorophyll and CDOM concentrations; and a fusion based capability to produce images and classifications of the shallow water seafloor. Adopting a multitasking approach, we expect to achieve computation of the point clouds, DEMs, and reflectance images at a 1:1 processing to acquisition ratio.

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“…Equation (1) can also represent the optical domain signal detected from arbitrary depth D by replacing ρ with β π (the water backscattering coefficient) and by treating the stretch factor as unity. The latter representation is often called the water column response [4]. Together, the water column response and the sea floor response form two parts of a piecewise function that characterizes the returned signal at any depth.…”

Section: Theory and Backgroundmentioning

confidence: 99%

Estimating field-of-view loss in bathymetric lidar: application to large-scale simulations

Carr

Tuell

2014

Appl. Opt.

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When designing a bathymetric lidar, it is important to study simulated waveforms for various combinations of system and environmental parameters. To predict a system's ranging accuracy, it is often necessary to analyze thousands of waveforms. In these large-scale simulations, estimating field-of-view loss is a challenge because the calculation is complex and computationally intensive. This paper describes a new procedure for quickly approximating this loss, and illustrates how it can be used to efficiently predict ranging accuracy.

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Predicted bathymetric lidar performance of Coastal Zone Mapping and Imaging Lidar (CZMIL)

Cited by 13 publications

References 0 publications

Predicted lidar ranging accuracy for CZMIL

Predicted lidar ranging accuracy for CZMIL

Conceptual design of the CZMIL data processing system (DPS): algorithms and software for fusing lidar, hyperspectral data, and digital images

Estimating field-of-view loss in bathymetric lidar: application to large-scale simulations

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