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Wave Parameters

Wave Parameters

The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...

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Updated: Jun 12, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

A marked point process for modeling lidar waveforms.

Clément Mallet¹, Florent Lafarge, Michel Roux

¹Université Paris-Est, IGN, Saint-Mandé, France. clement.mallet@ign.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 17, 2010

Summary

This summary is machine-generated.

This study introduces a new probabilistic model for analyzing Lidar waveforms, improving target characteristic retrieval. The method effectively reconstructs complex echoes using parametric curves, enhancing Lidar data analysis.

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Area of Science:

Geospatial Science
Signal Processing

Background:

Lidar waveforms are 1D signals from target reflections.
Accurate modeling is key for physical characteristic retrieval.

Purpose of the Study:

To present a novel probabilistic model for Lidar waveform reconstruction.
To fit waveform modes with suitable parametric functions for symmetric and asymmetric echoes.

Main Methods:

A marked point process model reconstructs discrete waveforms into parametric curves.
Includes data and regularization terms for model coherence and prior knowledge.
Reversible Jump Markov Chain Monte Carlo (RJMCMC) with simulated annealing explores the configuration space.

Main Results:

The model successfully fits both symmetric and asymmetric echoes.
Demonstrated high potential with various Lidar signals, especially from urban scenes.
Effective classification of actual laser scan data.

Conclusions:

The proposed probabilistic model offers a robust approach to Lidar waveform analysis.
Enables detailed reconstruction and characterization of targets from Lidar data.
Shows significant promise for applications in urban scene analysis and remote sensing.