A Marked Point Process for Modeling Lidar Waveforms

Mallet, Clément; Lafarge, Florent; Roux, Michel; Soergel, Uwe; Bretar, Frédéric; Heipke, Christian

doi:10.1109/tip.2010.2052825

Cited by 66 publications

(41 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future interesting work would be to implement other Configuration, Kernel and Energy models, in embedding spaces of other dimensions (1D signals (Hernandez-Marin et al, 2007;Mallet et al, 2010), 3D ellipses (Perrin et al, 2006)...) or even in a non-MPP context, for instance to optimize a 3D triangulation over the noisy heightfield of photogrammetric Digital Surface Model. Our wish is that the open-source license of the librjmcmc library will enable reproducible research.…”

Section: Discussionmentioning

confidence: 99%

Librjmcmc: An Open-Source Generic C++ Library for Stochastic Optimization

Brédif

Tournaire

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The librjmcmc is an open source C++ library that solves optimization problems using a stochastic framework. The library is primarily intended for but not limited to research purposes in computer vision, photogrammetry and remote sensing, as it has initially been developed in the context of extracting building footprints from digital elevation models using a marked point process of rectangles. It has been designed to be both highly modular and extensible, and have computational times comparable to a code specifically designed for a particular application, thanks to the powerful paradigms of metaprogramming and generic programming. The proposed stochastic optimization is built on the coupling of a stochastic Reversible-Jump Markov Chain Monte Carlo (RJMCMC) sampler and a simulated annealing relaxation. This framework allows, with theoretical guarantees, the optimization of an unrestricted objective function without requiring any initial solution.The modularity of our library allows the processing of any kind of input data, whether they are 1D signals (e.g. LiDAR or SAR waveforms), 2D images, 3D point clouds... The library user has just to define a few modules describing its domain specific context: the encoding of a configuration (e.g. its object type in a marked point process context), reversible jump kernels (e.g. birth, death, modifications...), the optimized energies (e.g. data and regularization terms) and the probabilized search space given by the reference process. Similar to this extensibility in the application domain, concepts are clearly and orthogonally separated such that it is straightforward to customize the convergence test, the temperature schedule, or to add visitors enabling visual feedback during the optimization. The library offers dedicated modules for marked point processes, allowing the user to optimize a Maximum A Posteriori (MAP) criterion with an image data term energy on a marked point process of rectangles.

show abstract

Section: Discussionmentioning

confidence: 99%

Librjmcmc: An Open-Source Generic C++ Library for Stochastic Optimization

Brédif

Tournaire

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…However, this method is considered challenging in the case of echoes with low signal strength (low SNR) and it is deficient in its calculation of the cross-section in complex waveforms. The method also requires initial determination of the number of targets [16,18,23,26], which is sometimes impractical or of high computational cost. In addition, a symmetric function might not always be sufficiently accurate to describe the target characteristics [17,27].…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…A low rate of height underestimation in the CHM generation was reported by the authors. A set of parametric pulse shapes (the Burr, Nakagami and generalized Gaussian models) have been considered by Mallet et al [23], where it was pointed out that the efficacy of the approach when applied to object classification was inadequate. However, more accurate results could be achieved when these waveform features were utilized in combination with geometric attributes.…”

Section: Decomposition Methodsmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

View full text Add to dashboard Cite

Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

show abstract

“…These models are described with respect to a reference point process, which is usually defined as the Poisson point process. In our model, we integrate interactions between adjacent objects by using Gibbs point processes, which are also applied in (Stoica et al, 2004), (Mallet et al, 2010) and others. In this setting the object configuration is described by a probability density function h which is related to a Gibbs energy U(.)…”

Section: Marked Point Processesmentioning

confidence: 99%

Network Detection in Raster Data Using Marked Point Processes

Schmidt¹,

Kruse²,

Rottensteiner³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.

show abstract

A Marked Point Process for Modeling Lidar Waveforms

Cited by 66 publications

References 49 publications

Librjmcmc: An Open-Source Generic C++ Library for Stochastic Optimization

Librjmcmc: An Open-Source Generic C++ Library for Stochastic Optimization

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

Network Detection in Raster Data Using Marked Point Processes

Contact Info

Product

Resources

About