Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > segmentation d'image
segmentation d'imageVoir aussi |
Documents disponibles dans cette catégorie (605)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Titre : Towards operational very high resolution land-cover mapping Titre original : Outils pour des occupations du sol opérationnelles à très haute résolution spatiale Type de document : Thèse/HDR Auteurs : Clément Mallet , Auteur Editeur : Champs/Marne : Université Paris-Est Année de publication : 2016 Importance : 144 p. Format : 21 x 30 cm Note générale : Bibliographie
Synthèse de travaux présentée en vue d’obtenir l’Habilitation à Diriger des Recherches délivrée par l’Université Paris-Est, spécialité « Sciences et Techniques de l’Information Géographique »Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'occupation du sol
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] données localisées 2D
[Termes IGN] données localisées 3D
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] information sémantique
[Termes IGN] reconstruction 3D
[Termes IGN] segmentation d'image
[Termes IGN] sémantisation
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) [Preamble] This document is the manuscript presented in order to obtain the Habilitation à Diriger des Recherches of Paris-Est University, France. My main professional activities, since my PhD defence in 2010, in the MATIS team of the French National Institute of Geographic and Forest Information (IGN), are described here. It may also include recall to previous works of the 2007-2010 period. I was recruited in the research department of IGN in 2005, being a civil servant. I first started as engineer on 3D airborne lidar point cloud processing in urban, natural, and seashore environments during the 2005-2007 period. Then, I focused on the emerging fullwaveform lidar technology during my PhD thesis, with a slight emphasis on urban areas (2007-2010). Since 2010, I took the lead of the research group that deals with land-cover mapping and change detection as well as 2D/3D geodatabase evaluation. Now, it encompasses more generally semantization and reconstruction issues for scene understanding. I naturally moved from airborne lidar data to any kind of geospatial/overhead remote sensing data (mainly satellite and airborne vertical imagery). Some of the developed methods has been eventually specialized to 3D terrestrial point clouds. [...] Note de contenu : PART ONE - SCIENTIFIC SYNTHESIS
1. INTRODUCTION
1.1 Relevance of remote sensing for land-cover mapping
1.2 A fastly evolving context for research in remote sensing
1.3 What is operational land-cover mapping?
1.4 Datasets
2. RESEARCH RESULTS 2011-2016
2.1 Remote sensing data processing
2.2 Feature extraction and selection
2.3 Object extraction/segmentation
2.4 Land-cover classification
2.5 Fusion
3. PERSPECTIVES
3.1 Efficient classifications
3.2 Semantic segmentation
3.3 Humans in the loop
3.4 Optimal data fusion
3.5 Exploitation of existing geospatial databases
3.6 Towards land-use mapping
3.7 Change detection and LC updating
3.8 Land-cover dynamics monitoring
PART TWO - CURRICULUM VITAE
4. EMPLOYEMENT AND EDUCATION
4.1 Civil status
4.2 Employement
4.3 Education
4.4 Awards
5. STUDENT SUPERVISION AND TEACHING
5.1 Supervision
6. CONTENTS
5.2 Teaching
6 Projects and collaborations
6.1 Funded projects
6.2 National and international cooperations
7. SCIENTIFIC ANIMATION
7.1 PhD committee membership
7.2 Editorial work and reviews
7.3 Conference and workshop organisation
7.4 Program committee membership
7.5 Scientific societies
8. PUBLICATION LIST
8.1 Book chapters
8.2 Journal papers
8.3 Peer-reviewed conference papers
8.4 MiscellaneousNuméro de notice : 22673 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : HDR Note de thèse : HDR : Sciences et Technologies de l’Information Géographique : UPE : 2016 Organisme de stage : MATIS (IGN) nature-HAL : HDR DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84127 Documents numériques
en open access
22673_Towards operational very high resolution land-cover mapping.pdfAdobe Acrobat PDF Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories / Shengli Tao in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)
[article]
Titre : Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Fangfang Wu, Auteur ; Qinghua Guo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 66 – 76 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] données lidar
[Termes IGN] écologie forestière
[Termes IGN] segmentation d'image
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data. Numéro de notice : A2015-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79444
in ISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 66 – 76[article]Superpixel-based graphical model for remote sensing image mapping / Guangyun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)
[article]
Titre : Superpixel-based graphical model for remote sensing image mapping Type de document : Article/Communication Auteurs : Guangyun Zhang, Auteur ; Xiuping Jia, Auteur ; Jiankun Hu, Auteur Année de publication : 2015 Article en page(s) : pp 5861 - 5871 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification contextuelle
[Termes IGN] classification pixellaire
[Termes IGN] décomposition du pixel
[Termes IGN] image multibande
[Termes IGN] modèle sémantique de données
[Termes IGN] segmentation d'imageRésumé : (Auteur) Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. Numéro de notice : A2015-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2423688 Date de publication en ligne : 08/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2423688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78826
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 5861 - 5871[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015111 SL Revue Centre de documentation Revues en salle Disponible Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm Type de document : Article/Communication Auteurs : Stelios Mylonas, Auteur ; Dimitris G. Stavrakoudis, Auteur ; John B. Theocharis, Auteur ; Paris A. Mastorocostas, Auteur Année de publication : 2015 Article en page(s) : pp 5352 - 5376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] classification floue
[Termes IGN] classification spectrale
[Termes IGN] regroupement de données
[Termes IGN] segmentation d'imageRésumé : (Auteur) In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. Numéro de notice : A2015-750 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421640 Date de publication en ligne : 08/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421640 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78759
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5352 - 5376[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture / S. Basu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture Type de document : Article/Communication Auteurs : S. Basu, Auteur ; Sangram Ganguly, Auteur ; Ramakrishna R. Nemani, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5690 - 5708 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] architecture des systèmes d'information
[Termes IGN] classification non dirigée
[Termes IGN] couvert forestier
[Termes IGN] Etats-Unis
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation d'imageRésumé : (Auteur) Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. Numéro de notice : A2015-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2428197 Date de publication en ligne : 26/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2428197 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78743
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5690 - 5708[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field / Miaozhong Xu in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)PermalinkA local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery / F. Cánovas-García in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkA fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkIntegrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkSemi-automated building footprint extraction from orthophotos / Rheannon Brooks in Geomatica, vol 69 n° 2 (June 2015)PermalinkPattern-mining approach for conflating crowdsourcing road networks with POIs / Bisheng Yang in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)PermalinkRefining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model / Min Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)PermalinkClassifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkA technique for simultaneous visualization and segmentation of hyperspectral data / Abhimitra Meka in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkStable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images / Julien Michel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)Permalink