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Termes IGN > imagerie
imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
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Titre : Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2017 Article en page(s) : pp 2799 - 2810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] anisotropie
[Termes IGN] correction radiométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage croisé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] intensité lumineuse
[Termes IGN] réflectance spectraleRésumé : (Auteur) The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimensionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications. Numéro de notice : A2017-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2654516 En ligne : https://doi.org/10.1109/TGRS.2017.2654516 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86392
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2799 - 2810[article]Mise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)
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Titre : Mise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques Type de document : Article/Communication Auteurs : Monique Moine, Auteur ; Henri Giraud, Auteur ; Anne Puissant, Auteur Année de publication : 2017 Article en page(s) : pp 13 - 23 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Alsace, plaine d'
[Termes IGN] cartographie automatique
[Termes IGN] classification orientée objet
[Termes IGN] image ALOS-PALSAR
[Termes IGN] occupation du sol
[Termes IGN] rétrodiffusion
[Termes IGN] signature polarimétrique
[Termes IGN] Vosges, massif desRésumé : (auteur) Les cartes d’occupation du sol produites à des résolutions spatiales et temporelles élevées constituent actuellement une ressource très importante pour beaucoup d’organismes privés ou publics. Le développement de méthodes de cartographie automatique, fiables et robustes basées sur la classification d’images satellites constitue ainsi un enjeu majeur. Dans ce cadre, l’imagerie radar apporte l’avantage de fournir des images de jour comme de nuit, et quelles que soient les conditions météorologiques. Plus récemment, l’exploitation des informations de rétrodiffusion fournies par les images SAR (Synthetic Aperture Radar) polarimétriques a permis d’étendre les possibilités apportées par l’imagerie radar. Dans cette étude, une carte d'occupation du sol a été produite sur une partie de la plaine d’Alsace et du massif vosgien à partir (1) de 76 paramètres polarimétriques extraits d’une image ALOS PALSAR en polarisation quadruple et (2) d’une méthode de classification orientée-objet. Plusieurs algorithmes de classification ont été testés et l'algorithme du plus proche voisin est ressorti comme donnant les meilleurs résultats. La méthode mise en place à l’avantage d’être semi-automatique et facilement reproductible. Neuf classes d’occupation du sol ont été cartographiées avec un taux de bon classement de 69%. Plus précisément, trois d'entre elles ont été très correctement détectées : la forêt, l’urbain et l’eau. D’autres classes ont été confondues du fait de la similarité de leur signature polarimétrique : les zones de vignobles, les prairies et les zones de cultures. Enfin, trois classes non visibles sur les données a priori et les images optiques de référence ont pu être identifiées sur l’image polarisée. Ces premiers résultats sont prometteurs pour la cartographie de l’occupation des sols à partir d’images SAR polarimétriques. Numéro de notice : A2017-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.52638/rfpt.2017.319 Date de publication en ligne : 16/08/2017 En ligne : https://doi.org/10.52638/rfpt.2017.319 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86546
in Revue Française de Photogrammétrie et de Télédétection > n° 215 (mai - août 2017) . - pp 13 - 23[article]Motion priors based on goals hierarchies in pedestrian tracking applications / Francisco Madrigal in Machine Vision and Applications, vol 28 n° 3-4 (May 2017)
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Titre : Motion priors based on goals hierarchies in pedestrian tracking applications Type de document : Article/Communication Auteurs : Francisco Madrigal, Auteur ; Jean-Bernard Hayet, Auteur Année de publication : 2017 Article en page(s) : pp 341 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] carrefour
[Termes IGN] compréhension de l'image
[Termes IGN] image vidéo
[Termes IGN] modèle de simulation
[Termes IGN] position
[Termes IGN] poursuite de cible
[Termes IGN] prévision
[Termes IGN] réalité de terrain
[Termes IGN] séquence d'imagesRésumé : (auteur) In this paper, the problem of automated scene understanding by tracking and predicting paths for multiple humans is tackled, with a new methodology using data from a single, fixed camera monitoring the environment. Our main idea is to build goal-oriented prior motion models that could drive both the tracking and path prediction algorithms, based on a coarse-to-fine modeling of the target goal. To implement this idea, we use a dataset of training video sequences with associated ground-truth trajectories and from which we extract hierarchically a set of key locations. These key locations may correspond to exit/entrance zones in the observed scene, or to crossroads where trajectories have often abrupt changes of direction. A simple heuristic allows us to make piecewise associations of the ground-truth trajectories to the key locations, and we use these data to learn one statistical motion model per key location, based on the variations of the trajectories in the training data and on a regularizing prior over the models spatial variations. We illustrate how to use these motion priors within an interacting multiple model scheme for target tracking and path prediction, and we finally evaluate this methodology with experiments on common datasets for tracking algorithms comparison. Numéro de notice : A2017-325 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-017-0832-8 Date de publication en ligne : 15/03/2017 En ligne : http://doi.org/10.1007/s00138-017-0832-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85384
in Machine Vision and Applications > vol 28 n° 3-4 (May 2017) . - pp 341 - 359[article]Retrieving spatial variations of land surface temperatures from satellite data–Cairo region, Egypt / Mohamed E. Hereher in Geocarto international, vol 32 n° 5 (May 2017)
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Titre : Retrieving spatial variations of land surface temperatures from satellite data–Cairo region, Egypt Type de document : Article/Communication Auteurs : Mohamed E. Hereher, Auteur Année de publication : 2017 Article en page(s) : pp 556 - 568 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] Egypte
[Termes IGN] image Terra-MODIS
[Termes IGN] Le Caire
[Termes IGN] température de surface
[Termes IGN] zone industrielle
[Termes IGN] zone ruraleRésumé : (Auteur) Cairo region is characterized by a range of physiographic features, including: flat agricultural lands, bare sandy deserts, highlands, calcareous terrains and urban land use. A time series data-set (300 images) acquired from the Moderate Resolution Imaging Spectroradiometer for the period July 2002–June 2015 were utilized to retrieve the spatial variations in the mean land surface temperature (LST) for the above-mentioned surface features. Results showed that vegetation, topography and surface albedo have negative correlations with LST. Vegetation/LST correlation has the maximum regression coefficient (R2 = 0.68) and albedo/LST has the minimum (R2 = 0.03). Cultivated lands reveal the lowest mean LST (40 °C) of Cairo region. There is a considerable urban heat island formed at Helwan south of Cairo, where heavy industries are settled. Industrial activities raised the mean LST of the region by at least 4 °C than the surrounding urban lands. Numéro de notice : A2017-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1161077 Date de publication en ligne : 17/03/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1161077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85301
in Geocarto international > vol 32 n° 5 (May 2017) . - pp 556 - 568[article]Self-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
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Titre : Self-taught feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ronald Kemker, Auteur ; Christopher Kanan, Auteur Année de publication : 2017 Article en page(s) : pp 2693 - 2705 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. Numéro de notice : A2017-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2651639 En ligne : https://doi.org/10.1109/TGRS.2017.2651639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86390
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2693 - 2705[article]Sentinel-1 interferometric SAR mapping of precipitable water vapor over a country-spanning area / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
PermalinkSuperpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
PermalinkTélédétection et photogrammétrie pour l'étude de la dynamique de l’occupation du sol dans le bassin versant de l’oued Chiba (Cap-Bon, Tunisie) / Anis Gasmi in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)
PermalinkUrban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data / Shailesh S. Deshpande in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)
PermalinkA comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkDetermining tree height and crown diameter from high-resolution UAV imagery / Dimitrios Panagiotidis in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)
PermalinkEfficient edge-aware surface mesh reconstruction for urban scenes / András Bódis-Szomorú in Computer Vision and image understanding, vol 157 (April 2017)
PermalinkEstimation of 3-D surface displacement based on InSAR and deformation modeling / Jun Hu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkEvaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)
PermalinkForest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)
PermalinkForestry applications of UAVs in Europe: a review / Chiara Torresan in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)
PermalinkHyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkMapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory / Jonas Bohlin in Silva fennica, vol 51 n° 2 (2017)
PermalinkMultilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
PermalinkSemantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery / Clément Dechesne in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
PermalinkSpatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
PermalinkStatistical atmospheric parameter retrieval largely benefits from spatial–spectral image compression / Joaquín García-Sobrino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkSurface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites / Seung-Bum Kim in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
PermalinkTrace coherence : a new operator for polarimetric and interferometric SAR images / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkTransferability of multi- and hyperspectral optical biocrust indices / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
PermalinkUnsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
PermalinkActive interseismic shallow deformation of the Pingting terraces (Longitudinal Valley – Eastern Taiwan) from UAV high-resolution topographic data combined with InSAR time series / Benoit Deffontaines in Geomatics, Natural Hazards and Risk, vol 8 (2017)
PermalinkAdaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkAssessment of textural differentiations in forest resources in Romania using fractal analysis / Ion Andronache in Forests, vol 8 n° 3 (March 2017)
PermalinkAttribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
PermalinkDerivation and validation of the high resolution satellite soil moisture products: a case study of the Biebrza Sentinel-1 validation sites / Jan Musiał in Geoinformation issues, Vol 8 n° 1 (2016)
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PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkDiscriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification / Lin He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkExtracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkGeometric accuracy evaluation of YG-18 satellite imagery based on RFM / Ruishan Zhao in Photogrammetric record, vol 32 n° 157 (March - May 2017)
PermalinkHyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkImage-based target detection and radial velocity estimation methods for multichannel SAR-GMTI / Kei Suwa in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkJoint inpainting of depth and reflectance with visibility estimation / Marco Bevilacqua in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)
PermalinkMapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory / Jonas Bohlin in Silva fennica, vol 51 n° 2 (2017)
PermalinkModified residual method for the estimation of noise in hyperspectral images / Asad Mahmood in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkNew point matching algorithm using sparse representation of image patch feature for SAR image registration / Jianwei Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkRefining geometry from depth sensors using IR shading images / Gyeongmin Choe in International journal of computer vision, vol 122 n° 1 (March 2017)
PermalinkRobust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkSatellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data / Manali Pal in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkSpatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery / Xiaobing Han in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
PermalinkThe right imagery for the job / Charlotte Bishop in GEO: Geoconnexion international, vol 16 n° 3 (March 2017)
PermalinkUnsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
PermalinkUsing vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings / Dongliang Wang in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)
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