Frédéric Bretar

A Marked Point Process for Modeling Lidar Waveforms

Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence ofparametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.

Terrain Modeling From Lidar Range Data in Natural Landscapes: A Predictive and Bayesian Framework

The Earths topography, including vegetation and human-made features, reduced to a virtual 3-D representation is a key geographic layer for any extended development or risk management project. Processed from multiple aerial images or from airborne lidar systems, the 3-D topography is first represented as a point cloud. This paper deals with the generation of digital terrain models (DTMs) in natural landscapes. We present a global methodology for estimating the terrain height by deriving a predictive filter paradigm. Under the assumption that the terrain topography (elevation and slope) is regular in a neighboring system, a predictive filter combines linearly the predicted topographic values and the effective measured values. In this paper, such a filter is applied to 3-D lidar data which are known to be of high elevation accuracy. The algorithm generates an adaptive local geometry wherein the elevation distribution of the point cloud is analyzed. Since local terrain elevations depend on the local slope, a predictive filter is first applied on the slopes and then on the terrain elevations. The algorithm propagates through the point cloud following specific rules in order to optimize the probability of computing areas containing terrain points. Considered as an initial surface, the previous DTM is finally regularized in a Bayesian framework. Our approach is based on the definition of an energy function that manages the evolution of a terrain surface. The energy is designed as a compromise between a data attraction term and a regularization term. The minimum of this energy corresponds to the final terrain surface. The methodology is discussed, and some conclusive results are presented on vegetated mountainous areas.

Solving the strip adjustment problem of 3D airborne lidar data

This paper proposes a contribution to the adjustment problem of airborne laser scanner strips. Based the comparison of each laser strips with a photogrammetric derived Digital Surface Model (DSM), and on the modeling of the discrepancies between these two data sets, the algorithm produces an homogeneous 3D deformation field. The measurements of these deformations is performed using a modified Hough transform and we search for the maximum of the accumulator whereupon we assign a probability of correctness. Depending of this probability, we estimate a global affine transform over the cloud of points. For transitivity properties, all strips are registered with regard to this DSM and therefore they all perfectly fit on overlapping areas. This fine registration is of importance for many applications, especially when fusionning photogrammetric data and lidar data for feature extraction. More generally, this method can be applied to any other sorts of 3D data and provides an efficient algorithm for matching 3D datasets

Comparison of RADARSAT-1 and IKONOS satellite images for urban features detection

The visual appearance of man-made objects in IKONOS and RADARSAT-1 satellite images is compared. Results show that for many mapping applications, the 9 m resolution RADARSAT-1 images add very limited information to the 1 m IKONOS panchromatic image. Despite these matters, it is shown that RADARSAT-1 may in some cases uniquely detect certain man-made objects or structures and thereby give additional knowledge to the interpretation of an IKONOS image. Multi-temporal RADARSAT-1 acquisitions can also be used to detect man-made changes at times when weather conditions hamper optical image acquisitions. It is expected that the forthcoming SAR satellites with resolution down towards 1 m, will work much better as a complementary, all-weather information source to the high-resolution optical ones.

Canopy Density Model: A New ALS-Derived Product to Generate Multilayer Crown Cover Maps

The canopy density model (CDM), a new product interpolated from airborne laser scanner (ALS) data and dedicated to forest structure characterization is presented. It exploits both the multiecho capability of the ALS and a nonparametric density estimation technique called kernel density estimators (KDEs). The CDM is used to delineate the outmost perimeter of vegetation features and to compute forest crown cover (CrCO). Contrary to other works that focus on single-layer forest canopies, CrCo is derived here for each layer, namely, the overstory, the understory, and ground vegetation. The root-mean-square error of prediction determined by using field data acquired over 44 forest stands in a forest in Portugal allows the testing of the reliability of the method: It ranges from 6.21% (overstory) to 13.76% (ground vegetation). In addition, we investigate the ability of the CDM to map the CrCo for individual trees. Finally, two existing methods have been applied to our study site in order to assess improvements, advantages, and drawbacks of our approach.

FullAnalyze: A Research tool for handling, processing and analyzing full-waveform lidar data

Full-waveform (FW) lidar systems provide range profiles of the Earth topography. They are acquired from airborne platforms or from satellites. Many applications derive from the use of such data, from the extraction of 3D point clouds to the inversion of vegetation profiles. Nevertheless, handling range profiles is much more difficult than handling 3D point cloud. The aim of this paper is to present a research tool based on opensource libraries that can process and visualize such data. We focused our work on the implementation on the 2D/3D interface that gives the possibility to visualize the interaction between the lidar electromagnetic waves and the Earth topography. Moreover, this tool integrates several processing steps of FW Lidar data.

Lidar waveform modeling using a marked point process

Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas.

Terrain modeling from lidar data: Hierarchical K-means filtering and Markovian regularization

Lidar 3D point cloud corresponds to the terrestrial topography, including true ground and objects belonging either to vegetated areas or to human made features. This paper deals with DTM (digital terrain model) production. First step filtering data into ground and off-ground points is based on a multi-resolution coarse-to-fine approach. The K-means algorithm is used in a hierarchical way that provides robust data filtering. The number of cluster splits is used to automatically qualify the filtering reliability. This point is rarely treated in previous works. Secondly, a regularization process over ground points generates an accurate DTM on a regular grid. The fine DTM is processed with ground points without using classical interpolation algorithms. In fact, a Markovian regularization minimizes a global energy that confronts the terrain regularity and the goodness of fit to the data. It also depends on the filtering reliability. Conclusive results are presented on vegetated and mountainous areas and provide realistic terrain models.

3D segmentation of forest structure using a mean-shift based algorithm

Consistent and accurate information on 3D forest canopy structure is required by many applications like forest inventory, management, logging, fuel mapping, habitat studies or biomass estimate. Compared to other remote sensing techniques (e.g., SAR or photogrammetry), airborne laser scanning is an adapted tool to provide such information by generating a three-dimensional georeferenced point cloud. Vertical structure analysis consists in detecting the number of layers within a forest stand and their limits. Until now, there is no approach that properly segments the different strata of a forest. In this study, we directly work on the 3D point cloud and we propose a mean shift (MS) based procedure for vertical forest segmentation. The approach that is carried out on complex forest plots improves the discrimination of vegetation strata.

Recognition of Building Roof Facets by Merging Aerial Images and 3D Lidar Data in a Hierarchical Segmentation Framework

We investigate in this paper an original methodology for detecting roof facets through the fusion of aerial images and lidar data (3D point cloud). Based on a hierarchical segmentation of the image, we define a cost function that manages the merging order of regions. It depends on both radio-metric similarities of two neighbouring regions as well as on extracted information from lidar data. Considering that lidar data have been filtered into points belonging either to ground or non-ground classes, we define semantic and geometric rules in the binary merging process. Building roof facets are finally detected by selecting a level of generality for representing roof building components. Some remarks are given concerning the reliability of the integration of lidar and image data. Reconstructed roof facets are finally shown onto complex buildings