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Automated conflation of digital elevation model with reference hydrographic lines / Timofey Samsonov in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
Titre : Automated conflation of digital elevation model with reference hydrographic lines Type de document : Article/Communication Auteurs : Timofey Samsonov, Auteur Année de publication : 2020 Article en page(s) : 40 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] alignement
[Termes descripteurs IGN] cartographie hydrographique
[Termes descripteurs IGN] conflation
[Termes descripteurs IGN] données localisées
[Termes descripteurs IGN] données vectorielles
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] réseau de drainage
[Termes descripteurs IGN] Triangulated Irregular Network
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation. Numéro de notice : A2020-297 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050334 date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.3390/ijgi9050334 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95135
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 40 p.[article]Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)
Titre : Learning and geometric approaches for automatic extraction of objects from remote sensing images Type de document : Thèse/HDR Auteurs : Nicolas Girard, Auteur Editeur : Nice : Université Côte d'Azur Année de publication : 2020 Importance : 169 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat Présentée en vue de l’obtention du grade de docteur en Automatique, Traitement du Signal et des Images de l'Université Côte d’AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] alignement
[Termes descripteurs IGN] appariement de données localisées
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] automatisation des processus
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] détection du bâti
[Termes descripteurs IGN] erreur
[Termes descripteurs IGN] figure géométrique
[Termes descripteurs IGN] filtrage du bruit
[Termes descripteurs IGN] jeu de données
[Termes descripteurs IGN] polygonation
[Termes descripteurs IGN] réalité de terrain
[Termes descripteurs IGN] segmentation d'image
[Termes descripteurs IGN] télédétection
[Termes descripteurs IGN] vectorisationRésumé : (auteur) Creating a digital double of the Earth in the form of a map has many applications in e.g. autonomous driving, automated drone delivery, urban planning, telecommunications, and disaster management. Geographic Information Systems (GIS) are the frameworks used to integrate geolocalized data and represent maps. They represent shapes of objects in a vector representation so that it is as sparse as possible while representing shapes accurately, as well as making it easier to edit than raster data. With the increasing amount of satellite and aerial images being captured every day, automatic methods are being developed to transfer the information found in those remote sensing images into Geographic Information Systems. Deep learning methods for image segmentation are able to delineate the shapes of objects found in images, but they do so with a raster representation, in the form of a mask. Post-processing vectorization methods then convert that raster representation into a vector representation compatible with GIS. Another challenge in remote sensing is to deal with a certain type of noise in the data, which is the misalignment between different layers of geolocalized information (e.g. between images and building cadaster data). This type of noise is frequent due to various errors introduced during the processing of remote sensing data. This thesis develops combined learning and geometric approaches with the purpose to improve automatic GIS mapping from remote sensing images. We first propose a method for correcting misaligned maps over images, with the first motivation for them to match, but also with the motivation to create remote sensing datasets for image segmentation with alignment-corrected ground truth. Indeed training a model on misaligned ground truth would not lead to a nice segmentation, whereas aligned ground truth annotations will result in better segmentation models. During this work we also observed a denoising effect of our alignment model and use it to denoise a misaligned dataset in a self-supervised manner, meaning only the misaligned dataset was used for training.
We then propose a simple approach to use a neural network to directly output shape information in the vector representation, in order to by-pass the post-processing vectorization step. Experimental results on a dataset of solar panels show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs. Our simple method is limited to predicting polygons with a fixed number of vertices though. While more recent methods for learning directly in the vector representation are not limited to a fixed number of vertices, they still have other limitations in terms of the type of object shapes they can predict. More complex topological cases such as objects with holes or buildings touching each other (with a common wall which is very typical of European city centers) are not handled by these fully deep learning methods. We thus propose a hybrid approach alleviating those limitations by training a neural network to output a segmentation probability map as usual and also to output a frame field aligned with the contours of detected objects (buildings in our case). The frame field constitutes additional shape information learned by the network. We then propose our highly parallelizable polygonization method for leveraging that frame field information to vectorize the segmentation probability map efficiently. Because our polygonization method has access to additional information in the form of a frame field, it can be less complex than other advanced vectorization methods and is thus faster. Lastly, requiring an image segmentation network to also output a frame field only adds two convolutional layers and virtually does not increase inference time, making the use of a frame field only beneficial.Note de contenu : 1- Introduction
2- Building alignment
3- Building alignment from noisy ground truth
4- PolyCNN: learning polygons
5- Frame field learning
6- Polygonization by frame field
7- Conclusions and perspectivesNuméro de notice : 28501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du Signal et des Images : Université Côte d’Azur : 2020 Organisme de stage : Inria Sophia-Antipolis En ligne : https://hal.inria.fr/tel-03111628/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96940
Titre : Analyse du bilan d’erreur appliquée aux systèmes de levés hydrographiques de surface et sous-marin Type de document : Article/Communication Auteurs : Geraud Naankeu-Wati, Auteur ; Jean-Baptiste Geldof, Auteur ; Pierre Bosser Année de publication : 2017 Article en page(s) : pp 53 - 63 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Bathymétrie
[Termes descripteurs IGN] alignement
[Termes descripteurs IGN] covariance
[Termes descripteurs IGN] erreur de mesure
[Termes descripteurs IGN] fond marin
[Termes descripteurs IGN] incertitude des données
[Termes descripteurs IGN] levé hydrographique
[Termes descripteurs IGN] positionnement inertiel
[Termes descripteurs IGN] propagation d'incertitude
[Termes descripteurs IGN] surface de l'eauRésumé : (auteur) Afin d'installer les infrastructures sous-marines (pipelines, puits sous-marins, etc.) nécessaires au développement des ressources en hydrocarbures, Total fait régulièrement appel aux compagnies hydrographiques. Ces entreprises effectuent principalement des campagnes d'acquisition de données (hydrographiques et géophysiques) à partir de deux types de systèmes : les systèmes de levé de surface et sous-marin. Durant la phase de préparation de ces campagnes, une estimation du bilan des erreurs du système d'acquisition de données est faite afin d'identifier tous les éléments qui affectent la qualité des données acquises, et de vérifier si l'incertitude sur la position de la sonde bathymétrique répond aux normes proposées par l'organisation hydrographique internationale (OHI) et Total. Cet article donne une analyse approfondie sur l'estimation du bilan d'erreur des systèmes de levé de surface et sous-marin tout en décrivant brièvement l'état de l'art de ces systèmes et en proposant de nouveaux algorithmes d'estimation du budget d'erreur appliqué, aux systèmes de levé de surface et sous-marin. Ces algorithmes se basent sur des modèles fonctionnels de ces systèmes, en prenant particulièrement en compte les termes de covariance entre les erreurs, les angles de désalignement entre la centrale inertielle et le sondeur et la latence entre les capteurs. Numéro de notice : A2017-592 Affiliation des auteurs : non IGN Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86760
in XYZ > n° 152 (septembre - novembre 2017) . - pp 53 - 63[article]Réservation
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Titre : Tectonic and anthropogenic deformation at the Cerro Prieto geothermal step-over revealed by sentinel-1A InSAR Type de document : Article/Communication Auteurs : Xiaohua Xu, Auteur ; David T. Sandwell, Auteur ; Ekaterina Tymofyeyeva, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5284 - 5292 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] alignement
[Termes descripteurs IGN] déformation de la croute terrestre
[Termes descripteurs IGN] faille géologique
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] interféromètrie par radar à antenne synthétique
[Termes descripteurs IGN] Mexique
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] tectoniqueRésumé : (Auteur) The Cerro Prieto geothermal field (CPGF) lies at the step-over between the imperial and the Cerro Prieto faults in northern Baja California, Mexico. While tectonically this is the most active section of the southern San Andreas Fault system, the spatial and temporal deformation in the area is poorly resolved by the sparse global positioning system (GPS) network coverage. Moreover, interferograms from satellite observations spanning more than a few months are decorrelated due to the extensive agricultural activity in this region. Here we investigate the use of frequent, short temporal baseline interferograms offered by the new Sentinel-1A satellite to recover two components of deformation time series across these faults. Following previous studies, we developed a purely geometric approach for image alignment that achieves better than 1/200 pixel alignment needed for accurate phase recovery. We construct interferometric synthetic aperture radar time series using a coherence-based small baseline subset method with atmospheric corrections by means of common-point stacking. We did not apply enhanced spectral diversity because the burst discontinuities are generally small ( Numéro de notice : A2017-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2704593 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2704593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87104
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5284 - 5292[article]
Titre : New point matching algorithm using sparse representation of image patch feature for SAR image registration Type de document : Article/Communication Auteurs : Jianwei Fan, Auteur ; Yan Wu, Auteur ; Fan Wang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1498 - 1510 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] alignement
[Termes descripteurs IGN] appariement de points
[Termes descripteurs IGN] chatoiement
[Termes descripteurs IGN] erreur de discrétisation
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] reconstruction automatique
[Termes descripteurs IGN] reconstruction d'image
[Termes descripteurs IGN] représentation parcimonieuseRésumé : (Auteur) Image registration is an important preprocessing step in many synthetic aperture radar (SAR) image applications. A key issue in image registration is to reliably establish the correspondences between the feature points extracted from the reference and sensed images. A new point matching algorithm is proposed in this paper to align two SAR images. In the proposed method, by considering image patches as the basic units, a novel local descriptor including the intensity and geometric information is assigned to each feature point, which is more robust to speckle noise. Furthermore, a correspondence establishment scheme is introduced based on the reconstruction errors between feature points calculated by the sparse representation (SR) technique, which is designed for achieving accurate matches. Based on the obtained SR coefficients, a coordinate correction procedure is further proposed for improving the localization accuracy of the obtained correspondences. Both simulated deformed and real SAR images are utilized to evaluate the performance. The experimental results indicate that the proposed method yields a better registration performance in terms of both accuracy and robustness. Numéro de notice : A2017-156 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2626373 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84692
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1498 - 1510[article]PermalinkPermalinkPermalinkPermalink
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