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QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
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Titre de série : QGIS in Remote Sensing, Volume 2 Titre : QGIS and applications in agriculture and forest Type de document : Monographie Auteurs : Nicolas Baghdadi, Éditeur scientifique ; Clément Mallet , Éditeur scientifique ; Mehrez Zribi, Éditeur scientifique
Editeur : Londres : ISTE Editions Année de publication : 2018 Importance : 374 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-119-45710-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] cartographie thématique
[Termes IGN] classification dirigée
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] QGIS
[Termes IGN] télédétection spatialeIndex. décimale : 37.35 Logiciels SIG Résumé : (Editeur) The QGIS in Remote Sensing Set aims to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing. This second volume of the set presents different applications of QGIS and its libraries for agriculture and forestry. A thorough knowledge of agriculture and forest areas is fundamental from both an economic and an environmental point of view. These environments are strongly involved in the use of spatial data, which are essential for restoring the spatio-temporal variability of surface conditions. In this context, GIS tools have long been used to support the exploitation of spatial imagery. This work is carried out by scientists who are proficient to a high level of technicality. The book is targeted at students (Masters, engineering students, PhDs), engineers and researchers who have already adopted geographic information systems. In addition to the text, readers will have access to data and tools allowing the integral realization of the scientific procedures described in each chapter, as well as screenshots of all the windows which illustrate the manipulations necessary for the realization of each application. Note de contenu : 1. Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas / Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi and Hassan Bazzi.
2. Disaggregation of Thermal Images / Mar Bisquert and Juan Manuel Sánchez.
3. Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB / Jean-Marc Gilliot, Camille Le Priol, Emmanuelle Vaudour and Philippe Martin.
4. Land Cover Mapping Using Sentinel-2 Images and the Semi-Automatic Classification Plugin: A Northern Burkina Faso Case Study / Louise Leroux, Luca Congedo, Beatriz Bellón, Raffaele Gaetano and Agnès Bégué.
5. Detection and Mapping of Clear-Cuts with Optical Satellite Images / Kenji Ose.
6. Vegetation Cartography from Sentinel-1 Radar Images / Pierre-Louis Frison and Cédric Lardeux.
7. Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park / Nicolas Karasiak and Pauline Perbet.
8. Physiognomic Map of Natural Vegetation / Samuel Alleaume and Sylvio Laventure.
9. Object-Based Classification for Mountainous Vegetation Physiognomy Mapping / Vincent Thierion and Marc Lang.Numéro de notice : 17567B Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif nature-HAL : DirectOuvrColl/Actes DOI : 10.1002/9781119457107 Date de publication en ligne : 19/01/2018 En ligne : http://dx.doi.org/10.1002/9781119457107 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91932 Voir aussi
- Utilisation de QGIS en télédétection, Volume 2. QGIS et applications en agriculture et forêt / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 3. QGIS and Applications in Territorial Planning / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 1. QGIS and generic tools / Nicolas Baghdadi (2018)
- Uso de QGIS en la teledetección, Vol. 2. QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)
QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
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Titre de série : QGIS in Remote Sensing, Volume 4 Titre : QGIS and Applications in Water and Risks Type de document : Monographie Auteurs : Nicolas Baghdadi, Éditeur scientifique ; Clément Mallet , Éditeur scientifique ; Mehrez Zribi, Éditeur scientifique
Editeur : Londres : ISTE Editions Année de publication : 2018 Importance : 320 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-119-47672-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] anthropisation
[Termes IGN] carte bathymétrique
[Termes IGN] cartographie thématique
[Termes IGN] données bathymétriques
[Termes IGN] érosion hydrique
[Termes IGN] image multibande
[Termes IGN] modèle numérique bathymétrique
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle RUSLE
[Termes IGN] QGIS
[Termes IGN] ressources en eau
[Termes IGN] risque naturel
[Termes IGN] télédétection spatiale
[Termes IGN] traitement d'imageIndex. décimale : 37.40 Applications SIG Résumé : (Editeur) The QGIS in Remote Sensing Set aims to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing. This fourth volume of the set is dedicated to the presentation and the practice of QGIS and its libraries of applications dealing with water and risk management issues. In the context of global changes (climatic and anthropogenic), understanding and quantifying water resource dynamics and the various aspects of risks is essential for managers in public authorities and local population. This work is carried out by scientists who are proficient to a high level of technicality. The book is targeted at students (Masters, engineering students, PhDs), engineers involved in the management of water resources and territory, and research teams in geomatics. In addition to the text, readers will have access to data and tools allowing the integral realization of the scientific procedures described in each chapter, as well as screenshots of all the windows which illustrate the manipulations necessary for the realization of each application. Note de contenu : 1. Monitoring Coastal Bathymetry Using Multispectral Satellite Images at High Spatial Resolution /
Bertrand Lubac.
2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) / Zeineb Kassouk, Zohra Lili-Chabaane, Benoit Deffontaines, Mohammad El Hajj and Nicolas Baghdadi.
3. Reservoir Hydrological Monitoring by Satellite Image Analysis / Paul Passy and Adrien Selles.
4. Network Analysis and Routing with QGIS / Hervé Pella and Kenji Ose.
5. Representation of the Drainage Network in Urban and Peri-urban Areas Using a 2D Polygonal Mesh Composed of Pseudo-convex Elements / Pedro Sanzana, Sergio Villaroel, Isabelle Braud, Nancy Hitschfeld, Jorge Gironas, Flora Branger, Fabrice Rodriguez, Ximena Vargas and Tomas Gomez.
6. Mapping of Drought / Mohammad El Hajj, Mehrez Zribi, Nicolas Baghdadi and Michel Le Page.
7. A Spatial Sampling Design Based on Landscape Metrics for Pest Regulation: The Millet Head Miner Case Study in the Bambey Area, Senegal / Valérie Soti.
8. Modeling Erosion Risk Using the RUSLE Equation / Rémi Andreoli.Numéro de notice : 17567D Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif nature-HAL : DirectOuvrColl/Actes DOI : 10.1002/9781119476726 Date de publication en ligne : 16/02/2018 En ligne : http://dx.doi.org/10.1002/9781119476726 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91931 Voir aussi
- Utilisation de QGIS en télédétection, Volume 4. QGIS et applications en eau et risques / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 3. QGIS and Applications in Territorial Planning / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 1. QGIS and generic tools / Nicolas Baghdadi (2018)
- Uso de QGIS en la teledetección, Vol. 4. QGIS y sus aplicaciones en agua y en gestion del riego / Nicolas Baghdadi (2020)
Rectified feature matching for spherical panoramic images / Tzu-Yi Chuang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 1 (January 2018)
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Titre : Rectified feature matching for spherical panoramic images Type de document : Article/Communication Auteurs : Tzu-Yi Chuang, Auteur ; N.H. Perng, Auteur Année de publication : 2018 Article en page(s) : pp 25 - 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] appariement d'images
[Termes IGN] image hémisphérique
[Termes IGN] image panoramiqueRésumé : (Auteur) Spherical panoramic image processing has received renewed interest in the fields of photogrammetry and computer vision. The difficulty in spherical matching is largely due to inevitable image distortions introduced from the equirectangular projection. In this paper, we present an effective strategy for tackling the problem of distortion to improve the performance of spherical image matching. The effectiveness of the rectified matching is evaluated with simulated data and compared with state-of-art methods. In addition, experiments with respect to matching between omnidirectional and planar images, establishing 2D-to-3D correspondence with lidar, and the pose estimation of a spherical image sequence are conducted. The results verify the utility of the proposed method, which provides stable and evenly distributed corresponding points, and it is suitable for integration with conventional techniques for further 3D exploitation of imagery. Numéro de notice : A2018-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.84.1.25 En ligne : https://doi.org/10.14358/PERS.84.1.25 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89168
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 1 (January 2018) . - pp 25 - 32[article]Réservation
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Titre : Robust hand pose recognition from stereoscopic capture Type de document : Thèse/HDR Auteurs : Rilwan Remilekun Basaru, Auteur Editeur : Londres : University of London Press Année de publication : 2018 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science, City, University of LondonLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands.
The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera.
The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels.
The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa.
Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.Numéro de notice : 17505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère En ligne : https://openaccess.city.ac.uk/19938/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90396 A stixel approach for enhancing semantic image segmentation using prior map information / Sylvain Jonchery (2018)
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Titre : A stixel approach for enhancing semantic image segmentation using prior map information Type de document : Article/Communication Auteurs : Sylvain Jonchery, Auteur ; Guillaume Bresson, Auteur ; Bruno Vallet , Auteur ; Rafal Żbikowski, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : ICARCV 2018, 15th International Conference on Control, Automation, Robotics and Vision 10/11/2018 21/11/2018 Singapour Singapour Proceedings IEEE Importance : pp 1715 - 1720 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] précision de la classification
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) A key problem for autonomous car navigation is the understanding, at an object level, of the current driving situation. Addressing this issue requires the extraction of meaningful information from on-board stereo imagery by classifying the fundamental elements of urban scenes into semantic categories that can more easily be interpreted and be reflected upon (streets, buildings, pedestrians, vehicles, signs, etc.). A probabilistic method is proposed to fuse a coarse prior 3D map data with stereo imagery classification. A novel fusion architecture based on the Stixel framework is presented for combining semantic pixel-wise segmentation from a convolutional neural network (CNN) with depth information obtained from stereo imagery while integrating coarse prior depth and label information. The proposed approach was tested on a manually labeled data set in urban environments. The results show that the classification accuracy of the fundamental elements composing the urban scene was significantly enhanced by this method compared to what is obtained from the semantic pixel-wise segmentation of a CNN alone. Numéro de notice : C2018-094 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICARCV.2018.8581150 Date de publication en ligne : 20/12/2018 En ligne : https://doi.org/10.1109/ICARCV.2018.8581150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94247 Superpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)
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