7 research outputs found
Data Fusion in a Hierarchical Segmentation Context: The Case of Building Roof Description
Automatic mapping of urban areas from aerial images is a challenging task for scientists an
Analyse et traitement d'ondes lidar pour la cartographie et la reconnaissance de formes : application au milieu urbain
International audienceToute onde lidar rétrodiffusée par la surface terrestre contient des informations sur les cibles atteintes ayant contribué à la forme de l'onde. Les systèmes lidar capables de numériser l'intégralité des signaux retour sont appa-rus récemment et permettent le traitement a posteriori de ces profils altimétriques. Nous présentons dans cet article une méthode d'analyse puis de traitement des ondes li-dar dans un contexte de cartographie automatique. Tout d'abord, nous montrons que l'analyse fine des ondes per-met une densification des nuages de points 3D. Dans un second temps, le traitement a posteriori des signaux conduit à leur modélisation sous forme paramétrique. Nous propo-sons alors une méthode de reconnaissance de formes ap-pliquée au milieu urbain. Une classification supervisée par Séparateurs à Vaste Marge est ainsi employée pour prendre en compte les caractéristiques des échos extraits lors de la phase de traitement. Les résultats montrent que la segmen-tation d'une zone urbaine en classes bâti, végétation, sol naturel et sol artificiel est possible à partir des ondes lidar seulement. Mots Clef Onde lidar, estimation paramétrique, modélisation, classification supervisée, milieu urbain. Abstract Each lidar waveform backscattered by the Earth's surface provide information about the reflecting targets giving its shape to the signal. Lidar systems are recently able to digitize the full-waveform and enable to post process these altimetric profiles. In this article, we present how lidar wa-veforms can be analysed and processed for automatic car-tographic purposes. First, fine waveform analysis allows to densify 3D point clouds. Besides, parametric signal modelling is carried out. Then, a image-based pattern recognition method for urban areas is proposed. A supervised classification using Support Vector Machines is therefore performed to use jointly the parameters extracted from the post processing step. Results show that it is possible to segment urban areas in building, vegetation, natural ground and artificial ground labels using only lidar waveforms
L-band InSAR decorrelation analysis in volcanic terrains using airborne LiDAR data and in situ measurements: The case of the Piton de la Fournaise volcano, France
An advanced photogrammetric method to measure surface roughness: Application to volcanic terrains in the Piton de la Fournaise, Reunion Island
International audienceWe present a rapid in situ photogrammetric method to characterize surface roughness by taking overlapping photographs of a scene. The method uses a single digital camera to create a high-resolution digital terrain model (pixel size of ~ 1.32 mm) by means of a free open-source stereovision software. It is based on an auto-calibration process, which calculates the 3D geometry of the images, and an efficient multi-image correlation algorithm. The method is successfully applied to four different volcanic surfaces - namely, a'a lava flows, pahoehoe lava flows, slabby pahoehoe lava flows, and lapilli deposits. These surfaces were sampled in the Piton de la Fournaise volcano (Reunion Island) in October, 2011, and displayed various terrain roughnesses. Our in situ measurements allow deriving digital terrain models that reproduce the millimeter-scale height variations of the surfaces over about 12 m2. Five parameters characterizing surface topography are derived along unidirectional profiles: the root-mean-square height (ξ), the correlation length (Lc), the ratio Zs = ξ2/Lc, the tortuosity index (τ), and the fractal dimension (D). Anisotropy in the surface roughness has been first investigated using 1-m-long profiles circularly arranged around a central point. The results show that Lc, Zs and D effectively catch preferential directions in the structure of bare surfaces. Secondly, we studied the variation of these parameters as a function of the profile length by drawing random profiles from 1 to 12 m in length. We verified that ξ and Lc increase with the profile length and, therefore, are not appropriate to characterize surface roughness variation. We conclude that Zs and D are better suited to extract roughness information for multiple eruptive terrains with complex surface texture