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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 IGN] alignement
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] erreur
[Termes IGN] figure géométrique
[Termes IGN] filtrage du bruit
[Termes IGN] jeu de données
[Termes IGN] polygonation
[Termes IGN] réalité de terrain
[Termes IGN] segmentation d'image
[Termes IGN] vectorisationIndex. décimale : THESE Thèses et HDR Résumé : (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 : Côte d’Azur : 2020 Organisme de stage : Inria Sophia-Antipolis nature-HAL : Thèse DOI : sans 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 : Low level feature detection in SAR images Type de document : Thèse/HDR Auteurs : Chenguang Liu, Auteur ; Florence Tupin, Directeur de thèse ; Yann Gousseau, Directeur de thèse Editeur : Paris [France] : Télécom ParisTech Année de publication : 2020 Importance : 138 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l’Institut Polytechnique de Paris préparée à Télécom Paris, Spécialité de doctorat : Signal, Images, Automatique et robotiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] gradient
[Termes IGN] image radar moirée
[Termes IGN] modèle de Markov
[Termes IGN] segment de droiteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In this thesis we develop low level feature detectors for Synthetic Aperture Radar (SAR) images to facilitate the joint use of SAR and optical data. Line segments and edges are very important low level features in images which can be used for many applications like image analysis, image registration and object detection. Contrarily to the availability of many efficient low level feature detectors dedicated to optical images, there are very few efficient line segment detector and edge detector for SAR images mostly because of the strong multiplicative noise. In this thesis we develop a generic line segment detector and an efficient edge detector for SAR images.The proposed line segment detector which is named as LSDSAR, is based on a Markovian a contrario model and the Helmholtz principle, where line segments are validated according to their meaningfulness. More specifically, a line segment is validated if its expected number of occurences in a random image under the hypothesis of the Markovian a contrario model is small. Contrarily to the usual a contrario approaches, the Markovian a contrario model allows strong filtering in the gradient computation step, since dependencies between local orientations of neighbouring pixels are permitted thanks to the use of a first order Markov chain. The proposed Markovian a contrario model based line segment detector LSDSAR benefit from the accuracy and efficiency of the new definition of the background model, indeed, many true line segments in SAR images are detected with a control of the number of false detections. Moreover, very little parameter tuning is required in the practical applications of LSDSAR. The second work of this thesis is that we propose a deep learning based edge detector for SAR images. The contributions of the proposed edge detector are two fold: 1) under the hypothesis that both optical images and real SAR images can be divided into piecewise constant areas, we propose to simulate a SAR dataset using optical dataset; 2) we propose to train a classical CNN (convolutional neural network) edge detector, HED, directly on the graident fields of images. This, by using an adequate method to compute the gradient, enables SAR images at test time to have statistics similar to the training set as inputs to the network. More precisely, the gradient distribution for all homogeneous areas are the same and the gradient distribution for two homogeneous areas across boundaries depends only on the ratio of their mean intensity values. The proposed method, GRHED, significantly improves the state-of-the-art, especially in very noisy cases such as 1-look images. Note de contenu : 1- Context
2- SAR basics, statistics of SAR images and data used in this thesis
I Line segment detection in SAR images
3- Introduction
4- LSD, a line segment detector with false detection control
5- LSDSAR, a generic line segment detector for SAR images
6- Experiments
II Edge detection in SAR images using CNNs
7- Introduction
8- Presentation of the HED method and of the training dataset
9- GRHED, introducing a hand-crafted layer before the usual CNNs
10- Experiments
11- Summary of the thesisNuméro de notice : 25878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Signal, Images, Automatique et robotique : Paris : 2020 nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02861903/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95689 A polyhedra-based model for moving regions in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)
[article]
Titre : A polyhedra-based model for moving regions in databases Type de document : Article/Communication Auteurs : Florian Heinz, Auteur ; Ralf Hartmut Güting, Auteur Année de publication : 2020 Article en page(s) : pp 41 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] base de données orientée objet
[Termes IGN] CGAL
[Termes IGN] implémentation (informatique)
[Termes IGN] isomorphisme
[Termes IGN] MADS
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] objet mobile
[Termes IGN] polyèdreRésumé : (auteur) Moving objects databases store and process objects with a focus on their spatiotemporal behaviour. To achieve this, the model of the data must be suitable to efficiently store and process moving objects. Currently, a unit-based model is widely used, where each moving object is divided into one or more time intervals, during which the object behaves uniformly. This model is also used for a data type called moving regions, which resembles moving and shape changing regions as, for example, forest fires or cloud fields. However, this model struggles to support operations like union, difference or intersection of two moving regions; the resulting objects are unnecessarily bloated and uncomfortable to handle because the resulting number of units is generally very high. In this paper, an alternative model for moving regions is proposed, which is based on polyhedra. Furthermore, this work develops an isomorphism between moving regions and polyhedra including all relevant operations, which has the additional advantage that several implementations for those are already readily available; this is demonstrated by a reference implementation using the existing and well-tested Computational Geometry Algorithms Library (CGAL). Numéro de notice : A2020-007 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1616090 Date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1616090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94387
in International journal of geographical information science IJGIS > vol 34 n° 1 (January 2020) . - pp 41 - 73[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020011 RAB Revue Centre de documentation En réserve L003 Disponible Robust pose estimation and calibration of catadioptric cameras with spherical mirrors / Sagi Filin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)
[article]
Titre : Robust pose estimation and calibration of catadioptric cameras with spherical mirrors Type de document : Article/Communication Auteurs : Sagi Filin, Auteur ; Grigory Ilizirov, Auteur ; Bashar Elnashef, Auteur Année de publication : 2020 Article en page(s) : pp 33 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] estimation de pose
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] flux lumineux
[Termes IGN] lentille
[Termes IGN] méthode robuste
[Termes IGN] miroir
[Termes IGN] reconstruction 3D
[Termes IGN] sphère
[Termes IGN] trilatérationRésumé : (Auteur) Catadioptric cameras broaden the field of view and reveal otherwise occluded object parts. They differ geometrically from central-perspective cameras because of light reflection from the mirror surface. To handle these effects, we present new pose-estimation and reconstruction models for imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which three methods are established to estimate the system parameters: a resection-based one, a trilateration-based one that introduces novel constraints that enhance accuracy, and a direct and linear transform-based one. The estimated system parameters exhibit improved accuracy compared to the state of the art, and analysis shows intrinsic robustness to the presence of a high fraction of outliers. We then show that 3D point reconstruction can be performed at accurate levels. Thus, we provide an in-depth look into the geometrical modeling of spherical catadioptric systems and practical enhancements of accuracies and requirements to reach them. Numéro de notice : A2020-050 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.1.33 Date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.14358/PERS.86.1.33 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94535
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 1 (January 2020) . - pp 33 - 44[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020011 SL Revue Centre de documentation Revues en salle Disponible An indoor navigation model and its network extraction / Filippo Mortari in Applied geomatics, Vol 11 n° 4 (December 2019)
[article]
Titre : An indoor navigation model and its network extraction Type de document : Article/Communication Auteurs : Filippo Mortari, Auteur ; Eliseo Clementini, Auteur ; Sisi Zlatanova, Auteur ; Liu Liu, Auteur Année de publication : 2019 Article en page(s) : pp 413–427 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] axe médian
[Termes IGN] CityGML
[Termes IGN] diagramme de Voronoï
[Termes IGN] espace topologique
[Termes IGN] extraction automatique
[Termes IGN] extraction de données
[Termes IGN] modèle géométrique du bâti
[Termes IGN] modèle numérique du bâti
[Termes IGN] modélisation 3D
[Termes IGN] positionnement en intérieur
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] représentation spatio-sémantiqueRésumé : (auteur) We propose a navigation model for indoor environments that combines a 3D geometric modeling of buildings with connection properties of spaces and semantic elements such as openings and installations. The model is an extension of the IndoorGML standard navigation module with a twofold benefit: the extension facilitated the data import from the international standard CityGML and introduced the semantics of various fixtures in indoor space of buildings making the navigation model more suitable for human needs. Several experiments have been conducted by extracting networks from CityGML data and performing a comparison with other network construction techniques. The second contribution of the paper is an algorithm for the automatic extraction of the navigation network. Such an algorithm is a hybrid solution between medial axis approaches and visibility graph approaches. Normally, medial axes approaches are a good representation of human navigation in narrow corridors, especially to avoid obstacles, but introduce distortions in open space. On the other hand, visibility approaches work better in open spaces. In our extraction technique, the resulting network takes advantages of both approaches and better mimics human beings’ navigation in indoor environments. Numéro de notice : A2019-534 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00273-8 Date de publication en ligne : 17/06/2019 En ligne : https://doi.org/10.1007/s12518-019-00273-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94129
in Applied geomatics > Vol 11 n° 4 (December 2019) . - pp 413–427[article]Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes / Antonio Tomás Mozas-Calvache in International journal of geographical information science IJGIS, vol 33 n° 11 (November 2019)PermalinkTroposphere delay modeling with horizontal gradients for satellite laser ranging / Mateusz Drożdżewski in Journal of geodesy, vol 93 n°10 (October 2019)PermalinkA method for drawing vertical curve in longitudinal profile in road project / Hüseyin İnce in Survey review, vol 51 n° 368 (September 2019)PermalinkEmpirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkLarge scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain / Convadonga Prendes in iForest, biogeosciences and forestry, vol 12 n° 4 (July 2019)PermalinkAutomatic sensor orientation using horizontal and vertical line feature constraints / Yanbiao Sun in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkBuilding detection and regularisation using DSM and imagery information / Yousif A. Mousa in Photogrammetric record, vol 34 n° 165 (March 2019)PermalinkCentral place indexing : hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems / Kevin Sahr in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkA generalized theory of the figure of the Earth : formulae / Chengli Huang in Journal of geodesy, vol 93 n° 3 (March 2019)PermalinkGeographic space as a living structure for predicting human activities using big data / Bin Jiang in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)Permalink