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Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases / Amin Alizadeh Naeini in Geocarto international, vol 35 n° 12 ([01/09/2020])
[article]
Titre : Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases Type de document : Article/Communication Auteurs : Amin Alizadeh Naeini, Auteur ; Sayyed Bagher Fatemi, Auteur ; Masoud Babadi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1311 - 1326 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] correction d'image
[Termes IGN] image Cartosat-1
[Termes IGN] image satellite
[Termes IGN] MNS ASTER
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'appui
[Termes IGN] précision géométrique (imagerie)Résumé : (auteur) Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs. Numéro de notice : A2020-481 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1573854 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1573854 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95632
in Geocarto international > vol 35 n° 12 [01/09/2020] . - pp 1311 - 1326[article]Post‐filtering with surface orientation constraints for stereo dense image matching / Xu Huang in Photogrammetric record, vol 35 n° 171 (September 2020)
[article]
Titre : Post‐filtering with surface orientation constraints for stereo dense image matching Type de document : Article/Communication Auteurs : Xu Huang, Auteur ; Rongjun Qin, Auteur Année de publication : 2020 Article en page(s) : pp 375-401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] filtrage numérique d'image
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] orientation d'imageRésumé : (Auteur) Dense image matching (DIM) is a critical technique when computing accurate 3D geometric information for many photogrammetric applications. Most DIM methods adopt first‐order regularisation priors for efficient matching, which often introduce stepped biases (also called fronto‐parallel biases) into the matching results. To remove these biases and compute more accurate matching results, this paper proposes a novel post‐filtering method by adjusting the surface orientation of each pixel in the matching process. The core algorithm formulates the post‐filtering as the optimisation of a global energy function with second‐order regularisation priors. A compromise solution of the energy function is computed by breaking the optimisation into a collection of sub‐optimisations of each pixel in a local adaptive window. The proposed method was compared with several state‐of‐the‐art post‐filtering methods on indoor, aerial and satellite datasets. The comparisons demonstrate that the proposed method obtains the highest post‐filtering accuracies on all datasets. Numéro de notice : A2020-437 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12333 Date de publication en ligne : 20/09/2020 En ligne : https://doi.org/10.1111/phor.12333 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95843
in Photogrammetric record > vol 35 n° 171 (September 2020) . - pp 375-401[article]Vehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
[article]
Titre : Vehicle detection of multi-source remote sensing data using active fine-tuning network Type de document : Article/Communication Auteurs : Xin Wu, Auteur ; Wei Li, Auteur ; Danfeng Hong, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 39 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Allemagne
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données multisources
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] segmentation
[Termes IGN] segmentation sémantique
[Termes IGN] véhiculeRésumé : (auteur) Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site. Numéro de notice : A2020-546 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.016 Date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95772
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 39 - 53[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
[article]
Titre : Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Zhendong Wang, Auteur ; Han Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] appariement de points
[Termes IGN] éclairage
[Termes IGN] image aérienne
[Termes IGN] image terrestre
[Termes IGN] maillage
[Termes IGN] milieu urbain
[Termes IGN] modèle stéréoscopique
[Termes IGN] séparateur à vaste marge
[Termes IGN] valeur aberranteRésumé : (auteur) Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy are still limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these problems, we propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching. The methods have linear time complexity with regard to the number of images. It explicitly handles low overlap using multi-view images. The proposed methods can be directly injected into off-the-shelf structure-from-motion (SFM) and multi-view stereo (MVS) solutions. First, aerial and ground images are reconstructed separately and initially co-registered through weak georeferencing data. Second, aerial models are rendered to the initial ground views, in which color, depth and normal images are obtained. Then, feature matching between synthesized and ground images are conducted through descriptor searching and geometry-constrained outlier removal. Finally, oriented 3D patches are formulated using the synthesized depth and normal images and the correspondences are propagated to the aerial views through patch-based matching. Experimental evaluations using five datasets reveal satisfactory performance of the proposed methods in aerial-ground image matching, which succeeds in all of the ten challenging pairs compared to only three for the second best. In addition, incorporation of existing SFM and MVS solutions enables more complete reconstruction results, with better internal stability. Numéro de notice : A2020-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.024 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95234
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 26 - 40[article]Réservation
Réserver ce documentExemplaires (3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Learning stereo reconstruction with deep neural networks Type de document : Thèse/HDR Auteurs : Stepan Tulyakov, Auteur ; François Fleuret, Directeur de thèse ; Anton Ivanov, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2020 Importance : 139 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée à l'Ecole Polytechnique Fédérale de Lausanne pour l’obtention du grade de Docteur ès SciencesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] contrainte géométrique
[Termes IGN] couple stéréoscopique
[Termes IGN] entropie
[Termes IGN] estimateur
[Termes IGN] étalonnage géométrique
[Termes IGN] modèle stéréoscopique
[Termes IGN] profondeur
[Termes IGN] réalité de terrain
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'image
[Termes IGN] vision par ordinateur
[Termes IGN] vision stéréoscopiqueRésumé : (auteur) Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these methods, is that they typically utilize a single depth cue, such as parallax, defocus blur or shading, and thus are not as robust as a human visual system that simultaneously relies on a range of monocular and binocular cues. This is mainly because it is hard to manually design a model, accounting for multiple depth cues. In this work, we address this problem by focusing on deep learning-based stereo methods that can discover a model for multiple depth cues directly from training data with ground truth depth. The complexity of deep learning-based methods, however, requires very large training sets with ground truth depth, which is often hard or costly to collect. Furthermore, even when training data is available it is often contaminated with noise, which reduces the effectiveness of supervised learning. In this work, in Chapter 3 we show that it is possible to alleviate this problem by using weakly supervised learning, that utilizes geometric constraints of the problem instead of ground truth depth. Besides the large training set requirement, deep stereo methods are not as application-friendlyas traditional methods. They have a large memory footprint and their disparity range is fixed at training time. For some applications, such as satellite stereo i magery, these are serious problems since satellite images are very large, often reaching tens of megapixels, and have a variable baseline, depending on a time difference between stereo images acquisition. In this work, in Chapter 4 we address these problems by introducing a novel network architecture with a bottleneck, capable of processing large images and utilizing more context, and an estimator that makes the network less sensitive to stereo matching ambiguities and applicable to any disparity range without re-training. Because deep learning-based methods discover depth cues directly from training data, they can be adapted to new data modalities without large modifications. In this work, in Chapter 5 we show that our method, developed for a conventional frame-based camera, can be used with a novel event-based camera, that has a higher dynamic range, smaller latency, and low power consumption. Instead of sampling intensity of all pixels with a fixed frequency, this camera asynchronously reports events of significant pixel intensity changes. To adopt our method to this new data modality, we propose a novel event sequence embedding module, that firstly aggregates information locally, across time, using a novel fully-connected layer for an irregularly sampled continuous domain, and then across discrete spatial domain. One interesting application of stereo is a reconstruction of a planet’s surface topography from satellite stereo images. In this work, in Chapter 6 we describe a geometric calibration method, as well as mosaicing and stereo reconstruction tools that we developed in the framework of the doctoral project for Color and Stereo Surface Imaging System onboard of ESA’s Trace Gas Orbiter, orbiting Mars. For the calibration, we propose a novel method, relying on starfield images because large focal lengths and complex optical distortion of the instrument forbid using standard methods. Scientific and practical results of this work are widely used by a scientific community. Note de contenu : 1- Introduction
2- Background
3- Weakly supervised learning of deep patch-matching cost
4- Applications-friendly deep stereo
5- Dense deep event-based stereo
6- Calibration of a satellite stereo system
7- ConclusionsNuméro de notice : 25795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2020 En ligne : https://infoscience.epfl.ch/record/275342?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95025 Occlusion probability in operational forest inventory field sampling with ForeStereo / Fernando Montes in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)PermalinkPairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkDouble projection planes method for generating enriched disparity maps from multi-view stereo satellite images / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 11 (November 2017)PermalinkA TV prior for high-quality scalable multi-view stereo reconstruction / Andreas Kuhn in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkDétection de l'érosion dans un bassin versant agricole par comparaison d'images multidates acquises par drone / Jonathan Lisein in Revue Française de Photogrammétrie et de Télédétection, n° 213 - 214 (janvier - avril 2017)PermalinkGeolocation error tracking of ZY-3 three line cameras / Hongbo Pan in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkDisaster debris estimation using high-resolution polarimetric stereo-SAR / Christian N. Koyama in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkBasal area and diameter distribution estimation using stereoscopic hemispherical images / Mariola Sánchez-González in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)PermalinkQuantitative estimation and validation of the effects of the convergence, bisector elevation, and asymmetry angles on the positioning accuracies of satellite stereo pairs / Jaehoon Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)PermalinkAssessment of orthoimage and DEM derived from ZY-3 stereo image in Northeastern China / Y. Dong in Survey review, vol 48 n° 349 (July 2016)Permalink