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Geometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)
Titre : Geometric and semantic joint approach for the reconstruction of digital models of buildings Type de document : Thèse/HDR Auteurs : Pierre-Alain Langlois, Auteur ; Renaud Marlet, Directeur de thèse ; Alexandre Boulch, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2021 Importance : 131 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l’Ecole des Ponts ParisTech, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconnaissance de surface
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] texture d'imageIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The advent of Building Information Models (BIM) in the field of construction and city management revolutionizes the way we design, build, operate and maintain our buildings. BIM models not only include the geometric aspect of the buildings but also semantic information which identifies its logical components (walls, slabs, windows, doors, etc..). While this information is fairly reasonable to create during the building design, only 1% of the building stock is renewed each year. There is therefore an increasing need for automated methods to generate BIM models on existing buildings from sensors such as simple RGB cameras or more advanced Lidar sensors which directly provide a point cloud.In this context, the goal of this thesis is to develop approaches for BIM reconstruction, including both geometric reconstruction and semantic analysis.These tasks have been explored, but an important research effort is conducted to make them more robust to the variety of use cases found in practice.3D reconstruction is usually operated based on direct 3D acquisitions such as Lidars or using photogrammetry, i.e., using pictures to triangulate key point locations and reconstruct the surface afterward. In the context of buildings, the later case is usually limited by the presence of textureless areas which make it hard for the algorithms to find key points and to triangulate them. Moreover, some parts of the buildings might be missing from the input data because of occlusions or omission from the acquisition operator.Regarding semantics in point clouds, important ambiguities exist between semantic classes: the discontinuity between a wall and a door can be hard to distinguish; a slab, a roof and a ceiling sometimes need additional context to be disentangled.In this thesis, we present three technical contributions to address these issues.First, for 3D reconstruction of building scenes, we propose the first method to reconstruct piecewise-planar scenes from images using line segments as primitives. While wide textureless areas exist in indoor scenes (e.g., walls), making it particularly difficult to detect key points, lines are often more visible and easier to detect (e.g., change of illumination at the intersection of two walls) and therefore should be used to ensure robustness of image-based reconstruction approaches. We leverage the presence of these line segments and the possibility to detect and triangulate them. This makes the method robust to textureless surfaces, and we show that we can reconstruct scenes on which point-based methods fail.The second contribution is more theoretical and addresses the problem of mesh reconstruction from multiple calibrated images of low resolution. In this context, traditional methods completely fail and directly learning priors on a large scale dataset of 3D shapes allows us to still perform reconstruction. More specifically, our method uses the learned priors to provide an initial rough shape which is further refined by incorporating geometric constraints. Our method directly outputs a mesh and competes with state of the art methods which can only output a noisy point cloud.Finally, the third technical contribution is VASAD, a dataset for volumetric and semantic reconstruction, which we created from raw BIM models available online. It is the first large scale dataset (62000m²) to offer both geometric and semantic annotation at point and mesh level. With this dataset, we propose two methods to jointly reconstruct both geometry and semantics from a point cloud and we show that the proposed dataset is challenging enough to stimulate research. Note de contenu : 1. Introduction
1.1 Motivation
1.2 Approach
1.3 Contributions
1.4 Organization of the dissertation
SURFACE RECONSTRUCTION FROM 3D LINE SEGMENTS
2. Introduction
2.1 Reconstructing textureless surfaces
2.2 Related Work
3. Method
3.1 Line extraction
3.2 Plane detection from 3D line segments
3.3 Surface reconstruction
4. Results
4.1 Datasets
4.2 Observations on the input data
4.3 Qualitative evaluation of reconstructions
4.4 Quantitative evaluation of reconstructions
4.5 Ablation study
4.6 Limitations and perspectives
4.7 Conclusion
3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING
5. Introduction
5.1 Learning 3D reconstruction
5.2 Related work
6. Method
6.1 Learning a Multi-View Parameterized Surface Mapping
6.2 Design choices
7. Results
7.1 Dataset
7.2 Evaluation criteria
7.3 Experimental results
7.4 Ablation study
7.5 Discussion and limitations
7.6 Conclusion
VASAD: A VOLUME AND SEMANTIC DATASET FOR BUILDING RECONSTRUCTION FROM POINT CLOUDS
8. Introduction
8.1 3D Reconstruction for buildings
8.2 Related work
9. DATASET
9.1 Building information models
9.2 Presentation of the dataset
9.3 3D representation
9.4 Point cloud simulation
9.5 Train/test split
10. Method
10.1 Reconstruction approaches
10.2 PVSRNet
10.3 Semantic Convolutional Occupancy Network
10.4 Data preparation
11. RESULTS
11.1 Metrics
11.2 Surface reconstruction
11.3 Semantic segmentation
11.4 Discussion
11.5 Conclusion
EPILOGUE
12. Conclusion
12.1 Looking back
12.2 Looking aheadNuméro de notice : 26822 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Champs-Sur-Marne : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard Monge LIGM nature-HAL : Thèse DOI : sans Date de publication en ligne : 11/04/2022 En ligne : https://tel.hal.science/tel-03637158/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100564
Titre : Unsupervised vision methods based on image perceptual information Type de document : Thèse/HDR Auteurs : Eric Bazan, Auteur ; Petr Dokladal, Directeur de thèse ; Eva Dokladalova, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, spécialité Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compréhension de l'image
[Termes IGN] contour
[Termes IGN] couleur (variable spectrale)
[Termes IGN] décomposition spectrale
[Termes IGN] filtre de Gabor
[Termes IGN] image captée par drone
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] visionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis work deals with extracting features and low-level primitives from perceptual image information to understand scenes. Motivated by the needs and problems in Unmanned Aerial Vehicles (UAVs) vision based navigation, we propose novel methods focusing on image understanding problems. This work explores three main pieces of information in an image: intensity, color, and texture. In the first chapter of the manuscript, we work with the intensity information through image contours. We combine this information with human perception concepts, such as the Helmholtz principle and the Gestalt laws, to propose an unsupervised framework for object detection and identification. We validate this methodology in the last stage of the drone navigation, just before the landing. In the following chapters of the manuscript, we explore the color and texture information contained in the images. First, we present an analysis of color and texture as global distributions of an image. This approach leads us to study the Optimal Transport theory and its properties as a true metric for color and texture distributions comparison. We review and compare the most popular similarity measures between distributions to show the importance of a metric with the correct properties such as non-negativity and symmetry. We validate such concepts in two image retrieval systems based on the similarity of color distribution and texture energy distribution. Finally, we build an image representation that exploits the relationship between color and texture information. The image representation results from the image’s spectral decomposition, which we obtain by the convolution with a family of Gabor filters. We present in detail the improvements to the Gabor filter and the properties of the complex color spaces. We validate our methodology with a series of segmentation and boundary detection algorithms based on the computed perceptual feature space. Numéro de notice : 15285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://hal.science/tel-03690309 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101418 Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
[article]
Titre : Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection Type de document : Article/Communication Auteurs : Chandi Witharana, Auteur ; Md Abul Ehsan Bhuiyan, Auteur ; Anna K. Liljedahl, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 174-191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] apprentissage profond
[Termes IGN] Arctique
[Termes IGN] artefact
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] fusion d'images
[Termes IGN] glace
[Termes IGN] image à haute résolution
[Termes IGN] pergélisol
[Termes IGN] texture d'imageRésumé : (Auteur) The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region. Numéro de notice : A2020-705 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.010 Date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96232
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 174-191[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)
[article]
Titre : Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery Type de document : Article/Communication Auteurs : Astrid Helena Huechacona-Ruiz, Auteur ; Juan Manuel Dupuy, Auteur ; Naomi B. Schwartz, Auteur Année de publication : 2020 Article en page(s) : n° 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] arbre caducifolié
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution spatiale
[Termes IGN] forêt tropicale
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] réflectance
[Termes IGN] texture d'image
[Termes IGN] YucatanRésumé : (auteur) In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery. Numéro de notice : A2020-756 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111234 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/f11111234 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96468
in Forests > vol 11 n°11 (November 2020) . - n° 1234[article]Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])
[article]
Titre : Textural classification of remotely sensed images using multiresolution techniques Type de document : Article/Communication Auteurs : Rizwan Ahmed Ansari, Auteur ; Krishna Mohan Buddhiraju, Auteur ; Avik Bhattacharya, Auteur Année de publication : 2020 Article en page(s) : pp 1580 - 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse multirésolution
[Termes IGN] analyse texturale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de contours
[Termes IGN] distance euclidienne
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image RVB
[Termes IGN] image satellite
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. Numéro de notice : A2020-618 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581263 Date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95994
in Geocarto international > vol 35 n° 14 [15/10/2020] . - pp 1580 - 1602[article]Thermal and spatial data integration for recreating rebuilding stages of wooden and masonry buildings / Paulina Lewińska in Photogrammetric record, vol 35 n° 171 (September 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)PermalinkRegion level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkAdaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkExtracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkSimultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkAutocovariance-based perceptual textural features corresponding to human visual perception / N. Abbadeni (2020)PermalinkPermalinkPermalinkUsing a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkMethod for an automatic alignment of imagery and vector data applied to cadastral information in Poland / Juan J. Ruiz-Lendínez in Survey review, vol 51 n° 365 (March 2019)PermalinkPermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)PermalinkRobust detection and affine rectification of planar homogeneous texture for scene understanding / Shahzor Ahmad in International journal of computer vision, vol 126 n° 8 (August 2018)PermalinkLarge scale textured mesh reconstruction from mobile mapping images and LIDAR scans / Mohamed Boussaha in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)PermalinkBinary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)Permalink