Descripteur
Documents disponibles dans cette catégorie (961)
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
Adaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Adaptive linear spectral mixture analysis Type de document : Article/Communication Auteurs : Chein-I Chang, Auteur Année de publication : 2017 Article en page(s) : pp 1240 - 1253 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image optique
[Termes IGN] signature spectraleRésumé : (Auteur) This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory. Numéro de notice : A2017-151 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2620494 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2620494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84683
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1240 - 1253[article]Hyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Hyperspectral SAR Type de document : Article/Communication Auteurs : Matthew Ferrara, Auteur ; Andrew J. Homan, Auteur ; Margaret Cheney, Auteur Année de publication : 2017 Article en page(s) : pp 1682 - 1695 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] image hyperspectrale
[Termes IGN] image radar moirée
[Termes IGN] réflectivité
[Termes IGN] traitement du signalRésumé : (Auteur) Typical synthetic aperture radar imaging techniques neglect the dispersive nature of the so-called image “reflectivity” function over the bandwidth of the transmitted waveform. In this paper, we form an image of the complex scene reflectivity as it depends on (x, y, and frequency), or equivalently (x, y, and time delay), a technique we refer to as hyperspectral synthetic aperture radar (HSAR). Our approach is based on a signal model that allows arbitrary flight trajectories and arbitrary waveforms (including continuously transmitting signals such as noise waveforms), and incorporates the causal, dispersive nature of the scene reflectivity without resorting to resolution-degrading frequency-domain sub-banding as others have previously proposed. We describe the resulting joint time-space resolution of HSAR in terms of the imaging point spread function for a selection of geometries and waveform bandwidths, and provide numerical examples to illustrate the approach. Numéro de notice : A2017-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2629265 En ligne : http://doi.org/10.1109/TGRS.2016.2629265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84697
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1682 - 1695[article]Joint inpainting of depth and reflectance with visibility estimation / Marco Bevilacqua in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)
[article]
Titre : Joint inpainting of depth and reflectance with visibility estimation Type de document : Article/Communication Auteurs : Marco Bevilacqua, Auteur ; Jean-François Aujol, Auteur ; Pierre Biasutti , Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 16 - 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte de profondeur
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image en couleur
[Termes IGN] inpainting
[Termes IGN] point caché
[Termes IGN] réflectance
[Termes IGN] semis de points
[Termes IGN] visibilitéRésumé : (Auteur) This paper presents a novel strategy to generate, from 3-D lidar measures, dense depth and reflectance images coherent with given color images. It also estimates for each pixel of the input images a visibility attribute. 3-D lidar measures carry multiple information, e.g. relative distances to the sensor (from which we can compute depths) and reflectances. When projecting a lidar point cloud onto a reference image plane, we generally obtain sparse images, due to undersampling. Moreover, lidar and image sensor positions typically differ during acquisition; therefore points belonging to objects that are hidden from the image view point might appear in the lidar images. The proposed algorithm estimates the complete depth and reflectance images, while concurrently excluding those hidden points. It consists in solving a joint (depth and reflectance) variational image inpainting problem, with an extra variable to concurrently estimate handling the selection of visible points. As regularizers, two coupled total variation terms are included to match, two by two, the depth, reflectance, and color image gradients. We compare our algorithm with other image-guided depth upsampling methods, and show that, when dealing with real data, it produces better inpainted images, by solving the visibility issue. Numéro de notice : A2017-073 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.01.005 Date de publication en ligne : 17/01/2017 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.01.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84310
in ISPRS Journal of photogrammetry and remote sensing > vol 125 (March 2017) . - pp 16 - 32[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017033 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017032 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Refining geometry from depth sensors using IR shading images / Gyeongmin Choe in International journal of computer vision, vol 122 n° 1 (March 2017)
[article]
Titre : Refining geometry from depth sensors using IR shading images Type de document : Article/Communication Auteurs : Gyeongmin Choe, Auteur ; Jaesik Park, Auteur ; Yu-Wing Tai, Auteur ; In So Kweon, Auteur Année de publication : 2017 Article en page(s) : pp 1 – 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] albedo
[Termes IGN] bande infrarouge
[Termes IGN] décomposition d'image
[Termes IGN] géométrie affine
[Termes IGN] image optique
[Termes IGN] Kinect
[Termes IGN] maille triangulaire
[Termes IGN] ombreRésumé : (auteur) We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements. Numéro de notice : A2017-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0937-y En ligne : https://doi.org/10.1007/s11263-016-0937-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85921
in International journal of computer vision > vol 122 n° 1 (March 2017) . - pp 1 – 16[article]Satellite-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)
[article]
Titre : Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data Type de document : Article/Communication Auteurs : Manali Pal, Auteur ; Rajib Maity, Auteur ; Mayank Suman, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1351 - 1362 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse en composantes principales
[Termes IGN] angle d'incidence
[Termes IGN] bande C
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Risat-1
[Termes IGN] modèle d'incertitude
[Termes IGN] polarimétrie radar
[Termes IGN] polarisation
[Termes IGN] teneur en eau liquideRésumé : (Auteur) This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land ( Numéro de notice : A2017-153 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2623378 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2623378 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84686
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1351 - 1362[article]Double take : mitigating interference with a dual-polarized antenna array in a real environment / Matteo Sgammini in GPS world, vol 28 n° 2 (February 2017)PermalinkHierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)PermalinkJoint analysis of passive and active land surface responses for Global Precipitation Measurement / Iris de Gelis (2017)PermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkRobust collaborative nonnegative matrix factorization for hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkSpectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing / Gregory P. Asner in Global ecology and conservation, vol 8 (October 2016)PermalinkA tensor decomposition-based anomaly detection algorithm for hyperspectral image / Xing Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkFloristic composition and across-track reflectance gradient in Landsat images over Amazonian forests / Javier Muro in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkImproving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo / Rong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)Permalink