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

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

International audienceIn contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms

MANAGING FULL WAVEFORM LIDAR DATA: A CHALLENGING TASK FOR THE FORTHCOMING YEARS

International audienceThis paper proposes to summarize researches and new advances in full waveform lidar data. After a description of full waveform lidar systems, we will review different methodologies developed to process the waveforms (modelling, correlation, stacking). Applications on urban and vegetated areas are then presented. The paper ends up with recommendations on future research themes

Traitement de données lidar à retour d'onde complète pour l'extraction de paramètres forestiers et de modèle numérique de terrain : validataion en forêt de conifères dans les Alpes

International audienceSmall footprint discrete return lidar data have already proved useful for providing information on forest areas. During the last decade, a new generation of airborne laser scanners, called full-waveform (FW) lidar systems, has emerged. They digitize and record the entire backscattered signal of each emitted pulse. Fullwaveform data hold large potentialities. In this study, we investigated the processing of raw full-waveform lidar data for deriving Digital Terrain Model (DTM) and Canopy Height Model (CHM). The main objective of this work was to compare geometric information derived from full-waveform and multi-echo data for various stands. An enhanced peak detection algorithm developed in a previous study was used to extract target positions from full-waveform data on plots under different stand characteristics. The resulting 3D point clouds were compared to the discrete return lidar observations provided by the lidar operator. Ground points were then identified using an original classification algorithm. They were used to derive DTMs which were compared to ground truth. Digital Surface Models were obtained from first echoes and canopy height models were then computed. Detecting weak echoes, when processing full-waveform data, enabled to better describe the canopy shape and to penetrate deeper into forest cover. However DTM was not significantly improved

Generating digital terrain model : joint use of airborne lidar data and optical images

The knowledge of an accurate topography is a prerequest for monitoring natural hazards and for environmental management (e.g. hydrologic and erosion models). Remote sensing lidar systems are active sensors which record altimeter data of the Earth’s surface as 3D point clouds : from an emitted laser pulse, the receptor detects backscaterred photons from the reflecting target. The altimeter accuracy is often higher than in the case of using stereoscopic conventional techniques, even if the ground density of the data is often lower. Additionnaly, beyond the altimeter information, these data contain spectral information related to the target reflectance in the infra-red domain. Depending on the target geometry, lidar systems can acquire several echos for a single laser pulse. This property is particularly interesting in forests or in areas of urban vegetation since it provides not only the canopy height, but also, under certain conditions, the terrain height under the vegetation layer....La représentation numérique de la surface topographique est une donnée de base pour appréhender toute gestion de l’environnement et des risques naturels. En complément de l’image traditionnelle riche en contenu sémantique, la télédétection active lidar fournit des données altimétriques de la surface terrestre à une précision encore inégalée par les techniques stéréoscopiques classiques. Sous la forme d’un nuage de points tri-dimensionnel, nous présentons dans cet article une méthode pour générer un Modèle Numérique de Terrain à partir de ces données lidar conjointement avec des données image. Nous nous intéressons particulièrement au milieux ruraux peu urbanisés pour lesquels une cartographie grande échelle est un enjeu majeur. L’algorithme que nous proposons est basé sur un filtrage prédictif de Kalman pour lequel la composante temporelle est remplacée par une indexation spatiale. Appliqué au calcul de la pente locale et de l’altitude du terrain, il s’agit de combiner linéairement une «mesure» basée sur l’analyse du nuage de points dans un environnement local cylindrique et une « prédiction » basée sur les calculs déjà effectués. Le facteur de combinaison linéaire est calculé en fonction des incertitudes respectives sur la «mesure» et sur la « prédiction » des états du système. Nous soulignons également l’importance de la prise en compte de la pente locale pour la détermination de la hauteur du sol. Si les données lidar fournissent parfois des informations altimétriques sur le terrain en présence de végétation, la densité de points au sol en présence de végétation dense devient très faible. Nous introduisons alors une adaptation du système de voisinage local basé sur l’intégration de données image et d’intensité lidar au sein d’un prédicteur de zones de végétation haute. Celui-ci s’accroît lorsque la densité de points au sol diminue, augmentant ainsi la probabilité de trouver des points sol. Nous présentons pour finir des résultats prometteurs pour la poursuite de ce travail sur le Golfe du Morbihan

Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

This article presents the use of new remote sensing data acquired from airborne fullwaveform lidar systems. They are active sensors which record altimeter profiles. This paper introduces a set of methodologies for processing these data. These techniques 5 are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within 10 badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionaly, beyond the altimeter information, full-waveform lidar data are processed to extract intensity and width of echoes. They are related to the target reflectance and geometry. Wa will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and 15 to investigate the potentiality of the intensity and width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. DTMs are then validated with field measurements. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The introduction of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is then discussed

Full waveform topographic lidar : state-of-the-art

We present a survey of the literature available on full-waveform lidar systems and resulting data. This survey concerns satellite and aerial remote sensing using active systems. Full-waveform airborne laser scanning theoretical principles are first described as well as their technological applications. Besides, main full-wave sensors and their specifications are presented. Furthermore, a review of digitized received signal processes and extracted data analysis are tackled. Eventually, topics of interest dealing with the interpretation of full-waveform measures are discussed, especially vegetation structural parameters estimation and forest and urban modeling, showing the potentiality of such data.Nous présentons une étude bibliographique sur les systèmes lidar topographiques à retour d’onde complète, aussi nommés lidar full-waveform, ainsi que sur les données générées. Le contexte de cette étude se situe dans le domaine de la télédétection aérienne et spatiale par systèmes actifs. Les bases théoriques de ces systèmes sont tout d’abord décrites ainsi que les applications technologiques induites. Ensuite, nous présentons les principaux capteurs à retour d’onde complète avec les spécifications techniques. Alors, à la lumière d’articles publiés dans la littérature scientifique, nous abordons les mécanismes de traitement des données brutes, modélisation et analyse quantitative des données extraites. Enfin, les thématiques d’applications liées à la cartographie des milieux forestiers et urbains sont évoquées, mettant en avant le potentiel de ces données lidar