Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques
extraction de traits caractéristiquesSynonyme(s)extraction des caractéristiques extraction de primitiveVoir aussi |
Documents disponibles dans cette catégorie (804)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery / Xiaobing Han in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
![]()
[article]
Titre : Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery Type de document : Article/Communication Auteurs : Xiaobing Han, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 195 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur non paramétrique
[Termes IGN] cohérence (physique)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (Auteur) The traditional spatial-spectral classification methods applied to hyperspectral remote sensing imagery are conducted by combining the spatial information vector and the spectral information vector in a separate manner, which may cause information loss and concatenation deficiency between the spatial and spectral information. In addition, the traditional morphological-based spatial-spectral classification methods require the design of handcrafted features according to experience, which is far from automatic and lacks generalization ability. To automatically represent the spatial-spectral features around the central pixel within a spatial neighborhood window, a novel spatial-spectral feature classification method based on the unsupervised convolutional sparse auto-encoder (UCSAE) with a window-in-window strategy is proposed in this study. The UCSAE algorithm features a unique spatial-spectral feature extraction approach which is executed in two stages. The first stage represents the spatial-spectral features within a spatial neighborhood window on the basis of spatial-spectral feature extraction of sub-windows with a sparse auto-encoder (SAE). The second stage exploits the spatial-spectral feature representation with a convolution mechanism for the larger outer windows. The UCSAE algorithm was validated by two widely used hyperspectral imagery datasets (the Pavia University dataset and the Washington DC Mall dataset) obtaining accuracies of 90.03 percent and 96.88 percent, respectively, which are better results than those obtained by the traditional hyperspectral spatial-spectral classification approaches. Numéro de notice : A2017-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.195 En ligne : https://doi.org/10.14358/PERS.83.3.195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84423
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 195 - 206[article]Building occlusion detection from ghost images / Guoqing Zhou in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
![]()
[article]
Titre : Building occlusion detection from ghost images Type de document : Article/Communication Auteurs : Guoqing Zhou, Auteur ; Yuefeng Wang, Auteur ; Tao Yue, Auteur Année de publication : 2017 Article en page(s) : pp 1074 - 1084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction d'image
[Termes IGN] détection de contours
[Termes IGN] détection de partie cachée
[Termes IGN] orthoimage
[Termes IGN] orthoimage intégrale
[Termes IGN] zone tamponRésumé : (Auteur) This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation. Numéro de notice : A2017-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2619184 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2619184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84634
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 1074 - 1084[article]On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)
![]()
[article]
Titre : On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings Type de document : Article/Communication Auteurs : Madhurima Bandyopadhyay, Auteur ; Jan Van Aardt, Auteur ; Kerry Cawse-Nicholson, Auteur ; Emmett Lentilucci, Auteur Année de publication : 2017 Article en page(s) : pp 123 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] fusion de données
[Termes IGN] image aérienne
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] zone urbaineRésumé : (Auteur) Three-dimensional (3D) data from light detection and ranging (lidar) sensor have proven advantageous in the remote sensing domain for characterization of object structure and dimensions. Fusion-based approaches of lidar and aerial imagery also becoming popular. In this study, aerial color (RGB) imagery, along with co-registered airborne discrete lidar data were used to separate vegetation and buildings from other urban classes/cover-types, as a precursory step towards the assessment of urban forest biomass. Both spectral and structural features such as object height, distribution of surface normals from the lidar, and a novel vegetation metric derived from combined lidar and RGB imagery, referred to as the lidar-infused vegetation index (LDVI) were used in this classification method. The proposed algorithm was tested on different cityscape regions to verify its robustness. Results showed a good separation of buildings and vegetation from other urban classes with on average an overall classification accuracy of 92 percent, with a kappa statistic of 0.85. These results bode well for the operational fusion of lidar and RGB imagery, often flown on the same platform, towards improved characterization of the urban forest and built environments. Numéro de notice : A2017-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.123 En ligne : https://doi.org/10.14358/PERS.83.2.123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84140
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 123 - 136[article]
contenu dans The 23rd international conference on MultiMedia Modeling, MMM 2017 / Laurent Amsaleg (2017)
Titre : Adaptive and optimal combination of local features for image retrieval Type de document : Article/Communication Auteurs : Neelanjan Bhowmik , Auteur ; Valérie Gouet-Brunet
, Auteur ; Lijun Wei
, Auteur ; Gabriel Bloch, Auteur
Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Collection : Lecture notes in Computer Science, ISSN 0302-9743 Projets : POEME / Da Silva, Jean-Claude Conférence : MMM 2017, 23rd international conference on Multimedia Modeling 04/01/2017 06/01/2017 Reykjavik Islande Proceedings Springer Importance : pp 76 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle de régression
[Termes IGN] point d'intérêt
[Termes IGN] recherche d'image basée sur le contenuRésumé : (Auteur) With the large number of local feature detectors and descriptors in the literature of Content-Based Image Retrieval (CBIR), in this work we propose a solution to predict the optimal combination of features, for improving image retrieval performances, based on the spatial complementarity of interest point detectors. We review several complementarity criteria of detectors and employ them in a regression based prediction model, designed to select the suitable detectors combination for a dataset. The proposal can improve retrieval performance even more by selecting optimal combination for each image (and not only globally for the dataset), as well as being profitable in the optimal fitting of some parameters. The proposal is appraised on three state-of-the-art datasets to validate its effectiveness and stability. The experimental results highlight the importance of spatial complementarity of the features to improve retrieval, and prove the advantage of using this model to optimally adapt detectors combination and some parameters. Numéro de notice : C2017-021 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-51814-5_7 Date de publication en ligne : 01/06/2017 En ligne : https://doi.org/10.1007/978-3-319-51814-5_7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88988 Documents numériques
peut être téléchargé
Adaptive and optimal combination - preprintAdobe Acrobat PDFFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)
Titre : Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas Type de document : Mémoire Auteurs : Cyril Wendl, Auteur ; Arnaud Le Bris , Encadrant
Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2017 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de stage, Ecole Polytechnique Fédérale de Lausanne EPFLLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] estimation bayesienne
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation
[Termes IGN] théorie de Dempster-ShaferIndex. décimale : MASTX Mémoires de masters divers Résumé : (auteur) Fusion of very high spatial resolution multispectral images with lower spatial resolution image time series having a higher number of bands can improve land use classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urbanized ground using decision-level fusion and regularization of individual classifications on Sentinel-2 and SPOT-6 satellite images. First, both images are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Decision-level fusion and regularization are used to combine the spatial and spectral advantages of both sources: First, both sources are merged in order to extract building labels with as high semantic and spatial precision as possible. Second, the building labels are used together with the Sentinel-2 classification as input for a binary classification of the artificialized area; the building labels from the regularization are dilated in order to connect the building objects and a binary classification is derived from the original Sentinel-2 classification before these two separate binary classifications are reintroduced in a fusion and regularization to find the artificialized area. Segmentation of the Sentinel-2 satellite image and majority voting of the object-level classification are also used to refine the contours of the artificialized area. Note de contenu : Introduction
1 - Methodology
2 - Artificialized area
3 - Results
ConclusionNuméro de notice : 21702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Rapport de stage Organisme de stage : MATIS (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90951 Documents numériques
peut être téléchargé
Fusion of Multi-Temporal ... pdf auteur -Adobe Acrobat PDF
peut être téléchargé
Fusion of Multi-Temporal... - pdf auteur -Adobe Acrobat PDFMise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017)
PermalinkRaft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)
PermalinkPermalinkPermalinkClass-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
PermalinkUrban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)
PermalinkGuided superpixel method for topographic map processing / Qiguang Miao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
PermalinkAutomatic registration of MLS point clouds and SfM meshes of urban area / Reiji Yoshimura in Geo-spatial Information Science, vol 19 n° 3 (October 2016)
PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkA robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data / Hamid Hamraz in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)
Permalink