Urban street mapping using quickbird and Ikonos Images

International audienceThis article addresses the problem of urban street mapping from new high resolution satellite images. The proposed algorithm is divided in two sequential modules: a topologically correct graph of the street network is first extracted, and streets are then extracted as surface elements. The graph of the network is extracted by a following algorithm which minimizes a cost function. The surface extraction algorithm makes use of specific active contours (snakes) combined with a multiresolution analysis (MRA) for minimizing the problem of noise. This reconstruction phase is composed of two steps: the extraction of street segments and the extraction of street intersections. Results of the street network extraction are presented on Quickbird and Ikonos images. Future prospects are also expose

Extraction de réseaux de rues à partir d'images satellites à haute résolution spatiale

- Cet article traite le problème de l'extraction de réseaux de rues à partir des nouvelles images satellites à haute résolution spatiale. La méthode proposée se décompose en deux modules séquentiels: un graphe topologiquement correct du réseau de rues est tout d'abord extrait, puis les rues sont ensuite extraites en tant qu'éléments de surface. Le graphe topologique du réseau peut être extrait de manière automatisée (par algorithme de suivi minimisant une fonction de coût) ou provenir d'une base de données. L'algorithme d'extraction surfacique des rues fait intervenir des contours actifs combinés à une analyse multirésolution afin d'accélérer la convergence de l'algorithme et de minimiser le problème du bruit géométrique. Cette phase de reconstruction comprend deux étapes séquentielles: l'extraction des rues puis le traitement des intersections. Des résultats de l'extraction de réseaux de rues sont présentés afin d'illustrer les différentes phases de la méthode et les perspectives de recherche sont exposées

Fine-Grained Action Detection and Classification in Table Tennis with Siamese Spatio-Temporal Convolutional Neural Network

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Extraction de réseaux de rues en milieu urbain à partir d'images satellites à très haute résolution spatiale

This report proposes a method for extracting urban street networks from new very high spatial resolution satellite images. Its goal is to meet the need for an automatized building of maps.the proposed method uses digital image as only input data. It is semi-automatic at the detection step, and takes advantage of cooperation between linear representation of streets and their representation as surface elements. A topological graph of the street network is first extracted, and used for initializing the surface reconstruction step. The extraction result can then be used in order to precisely register the street centerline. This method favors strong geometrical constraints in order to avoid a radiometric profile model of the street, too variable in urban areas. To that aim, a model of active contour associated with the wavelet transform, called doublesnake, has been developed. Its evolution in a multi-scale framework enables the extraction of parallel street sides in a noisy environment. Then, final positions of doublessnakes permit the extraction of intersections.the method has been applied on images from different sensors and with different urban types. An innovative protocol for a quantitative assessment of the results compared to human interpretation has shown its generic aspect, as well as its robustness with respect to noise.this method is a step toward a fully automatized cartography of the street network.La disponibilité d'images satellites à très haute résolution spatiale au dessus de zones urbaines est récente. Elle constitue potentiellement un très grand apport pour la cartographie des villes à des échelles de l'ordre du 1:10 000. La très haute résolution spatiale permet une représentation réelle des rues sue une carte, mais engendre une augmentation significative du bruit. Dans cette thèse, nous proposons une méthode d'extraction des réseaux de rues en milieu urbain à partir des images à très haute résolution spatiale. Son objectif est de répondre à une forte demande dans la création automatisée de cartes. La méthode proposée n'utilise que l'image numérique comme source d'information. Elle est semi-automatique au niveau de la détection et exploite la coopération entre la représentation linéique de la rue et sa représentation surfacique. Le graphe topologique du réseau est d'abord extrait et est utilisé pour initialiser l'étape de reconstruction surfacique. Le résultat d'extraction peut alors servir à recaler le graphe précisément sur l'axe des rues. La méthode utilise des contraintes géométriques fortes afin de ne pas dépendre d'un modèle de profil radiométrique de la rue, trop variable en milieu urbain. Dans cette optique, un modèle de contours actif associé à la transformée en ondelettes, le DoubleSnake, a été développé. Son évolution dans un cadre multi-échelle permet d'extraire les sections de rues à bords parallèles dans un environnement bruité. Les positions finales des DoubleSnakes permettent ensuite l'extraction des intersections. La méthode a été appliquée à des images de différents capteurs et avec différents types d'urbanisation. Un protocole innovant d'évaluation quantitative des résultats par comparaison à l'interprétation humaine a permis de montrer le caractère générique de la méthode, ainsi que sa bonne robustesse vis-à-vis du bruit. Cette méthode constitue un pas vers une cartographie automatisée du réseau de rues urbain

Potentialités des nouveaux capteurs à très haute résolution spatiale pour l'extraction des réseaux de rues urbains

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Multiresolution snakes for urban road extraction from Ikonos and QuickBird

International audienceThis article addresses the problem of street extraction in high resolution images from a topologically correct graph of the network. The extraction algorithm makes use of specific active contours (snakes) combined with a multiresolution analysis (MRA). The use of the MRA for computing the snake's energy enables to increase the convergence of the algorithm by minimizing the problem of noise (vehicles, ground markings,. . . ). This phase is composed of two sequential steps: the extraction of street segments and the extraction of street intersections. Indeed, these two objects present too many differences in both topology and shape to be processed in the same way. Results of the street network extraction are presented in order to illustrate the different steps of the method and future prospects are exposed. 1 ROAD NETWORK EXTRACTION 1.1 State of the art Road extraction from remotely sensed images has been the purpose of many works in the image processing field, and because of its complexity, is still a challenging topic. These methods are based on generic tools of image processing, such as linear filtering (Wang and Howarth 1987), mathematical morphology (Destival 1987), Markov fields (Merlet and Z´erubia 1996), neural networks (Bhattacharya and Parui 1997), dynamic programming (Gruen and Li 1995), or multiresolution analysis (Baumgartner et al. 1999; Couloigner and Ranchin 2000). Road models are common for all authors, i.e. the radiometry along one road is relatively homogeneous and contrasted compared to its background. Moreover the width of the road and its curvature are supposed to vary slowly, and the road network is supposed to be connex. Promising studies try to take the context of the road into account in order to focus the extraction on the most promising regions (Baumgartner et al. 1999). The recent possibility to have satellite images with a very high spatial resolution (1 meter or less) has reboosted the interest for road extraction (especially for the applications in urban areas). This increased resolution enables a more accurate localization of the road sides as well as its extraction as a surface element. In return, it generates a higher complexity of the image and an increase of geometri

Evaluation quantitative de méthodes d'extraction de rues : choix d'une référence de comparaison

International audienceIn the context of an automatic street extraction from remotely sensed images, we are concerned about defining some quantitative criteria for evaluating the performance of algorithms. The aim is to obtain a measure of the quality of the results delivered by any method. This purpose needs the definition of a reference in order to compare it with extracted objects. In this paper, existing references in matter of pattern recognition from geographical scenes will be reviewed. Then a methodology for extracting a relative reference is defined when the only information available is the remotely sensed image. Related quantitative criteria are also given. The complete methodology is finally applied on a high resolution image from an urban scene

Assessment of road extraction methods from satellite images : reflections and case study on the definition of a reference

International audienceIn the context of an automatic street extraction algorithm from remotely sensed images, we are concerned about defining some quantitative criteria for evaluating the performance of our method. The aim is to obtain quality measures of the results delivered by a method. This purpose needs the definition of a reference in order to compare it with extracted objects. In this paper, existing references in matter of road extraction from geographical scenes are reviewed. Then a methodology for defining a reference is proposed. The complete methodology is applied on a high resolution image from the IKONOS satellite. Conclusions are drawn enhancing the benefits of such an approach and future prospects are discussed

Road networks derived from high spatial resolution satellite remote sensing data

Book chapter partially available at: http://books.google.fr/books?id=WjbUuuliObQC&pg=PA215&lpg=PA215&dq=Road+networks+derived+from+high+spatial+resolution+satellite+remote+sensing+data.&source=bl&ots=V3hvzP7rGZ&sig=oX9rhQ_Jz3BdGoX4SDP0liL0p_U&hl=fr&sa=X&oi=book_result&resnum=1&ct=result#PPA215,M1There is a strong demand for accurate and up-to-date road network information. Road network knowledge is crucial for the creation and the update of maps, geographic information system (GIS) database, transportation or land planning. For local authorities, cartography of the road network is needed for urban planning, dirty water collection through gutter network (most often located under roads), traffic flow analysis or pollution mapping. Closely related applications are geo-marketing, electricity and telecommunication networks, databases for car navigation ... Currently, road network cartography is essentially done by human interpretations of high resolution aerial images and additional in situ information. This is a long and tedious work that requires to be done again for each update of the road network. High spatial resolution imagery is recently available for civilian applications and reveals the very fine details of the imaged area. Examples of high resolution satellites are SPOT 5, Ikonos, Quickbird, OrbView or EROS. The term ‘high resolution' is relative and refers to satellites with spatial resolutions better than 5 meters in the panchromatic channel (one can even talk about very high resolution when the image resolution is better than 1 meter). The current availability of high spatial resolution images represents an undeniable asset to Earth observation. The urban environment, that is the most difficult context because of its high complexity and information density, could benefit the most from high resolution imagery (Puissant and Weber, 2002). In addition to the increased precision for the road detection and location, high resolution satellite imagery can be used for numerous cases where the access to the studied area is difficult: administrative constraints, authorization to overfly the area, conflicts, wars or natural catastrophes... Moreover, satellite means is significantly cheaper than aerial or in situ data acquisition campaigns. As promising as it is, the use of high resolution images for road extraction induces a change in the road representation, and a significant increase of noise. Moreover, quantitative assessment of the results has to be redesigned when dealing with such images. In this chapter, a new method suitable for high resolution images is proposed. Originally designed for urban area, this method can naturally be applied on easier cases such as rural or semi-rural areas. The chapter is structured as follow: the change in the road representation induces by high resolution imagery is first presented. A short survey on road extraction with the evolution from linear to surface models for road is proposed (section 2). A new method for extracting road networks from high spatial resolution images is then described. It models roads as a surface and is built on cooperation between linear and surface representation of roads. In order to overcome local artifacts, the method makes use of advanced image processing tools, such as active contours and the wavelet transform (section 3). An example of application of the method on a high resolution image from the Quickbird satellite is proposed. The result is quantitatively assessed compared to human interpretation (section 4). This chapter concludes with a discussion on the principal benefits of the method and on future prospects (section 5)