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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Retrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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[article]
Titre : Retrieval of leaf area index in different plant species using thermal hyperspectral data Type de document : Article/Communication Auteurs : Elnaz Neinavaz, Auteur ; Andrew K. Skidmore, Auteur ; Roshanak Darvishzadeh, Auteur ; Thomas A. Groen, Auteur Année de publication : 2016 Article en page(s) : pp 390 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Buxus sempervirens
[Termes IGN] classification par réseau neuronal
[Termes IGN] espèce végétale
[Termes IGN] Euonymus japonicus
[Termes IGN] image hyperspectrale
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] méthode des moindres carrés
[Termes IGN] photo-interprétation
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] régression
[Termes IGN] Rhododendron (genre)Résumé : (Auteur) Leaf area index (LAI) is an important variable of terrestrial ecosystems because it is strongly correlated with many ecosystem processes (e.g., water balance and evapotranspiration) and directly related to the plant energy balance and gas exchanges. Although LAI has been accurately predicted using visible and short-wave infrared hyperspectral data (0.3–2.5 μm), LAI estimation using thermal infrared (TIR, 8–14 μm) measurements has not yet been addressed. The novel approach of this study is to evaluate the retrieval of LAI using TIR hyperspectral data. The leaf area indices were destructively acquired for four plant species: Azalea japonica, Buxus sempervirens, Euonymus japonicus, and Ficus benjamina. Canopy emissivity spectral measurements were obtained under controlled laboratory conditions using a MIDAC (M4401-F) spectrometer. The LAI retrieval was assessed using a partial least squares regression (PLSR), artificial neural networks (ANNs), and narrow band indices calculated from all possible combinations of waveband pairs for three vegetation indices including simple difference, simple ratio, and normalized difference. ANNs retrieved LAI more accurately than PLSR and vegetation indices (0.67 Numéro de notice : A2016-789 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82505
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 390 - 401[article]Satellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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Titre : Satellite images analysis for shadow detection and building height estimation Type de document : Article/Communication Auteurs : Gregoris Liasis, Auteur ; Stavros Stavrou, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 450 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] contour
[Termes IGN] délimitation
[Termes IGN] détection d'ombre
[Termes IGN] filtre spectral
[Termes IGN] hauteur du bâti
[Termes IGN] image satellite
[Termes IGN] intensité lumineuse
[Termes IGN] ombre
[Termes IGN] scène urbaine
[Termes IGN] valeur radiométriqueRésumé : (Auteur) Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation. Numéro de notice : A2016-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82509
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 437 - 450[article]Semiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
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Titre : Semiblind hyperspectral unmixing in the presence of spectral library mismatches Type de document : Article/Communication Auteurs : Xiao Fu, Auteur ; Wing-Kin Ma, Auteur ; José M. Bioucas-Dias, Auteur ; Tsung-Han Chan, Auteur Année de publication : 2016 Article en page(s) : pp 5171 - 5184 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] itération
[Termes IGN] régressionRésumé : (Auteur) The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects. Numéro de notice : A2016-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2557340 En ligne : https://doi.org/10.1109/TGRS.2016.2557340 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83087
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5171 - 5184[article]Shadow detection and removal in RGB VHR images for land use unsupervised classification / A. Movia in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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Titre : Shadow detection and removal in RGB VHR images for land use unsupervised classification Type de document : Article/Communication Auteurs : A. Movia, Auteur ; A. Beina, Auteur ; F. Crosilla, Auteur Année de publication : 2016 Article en page(s) : pp 485 - 495 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image numérique
[Termes IGN] analyse procustéenne
[Termes IGN] anisotropie
[Termes IGN] classification non dirigée
[Termes IGN] détection d'ombre
[Termes IGN] détection de changement
[Termes IGN] factorisation de Cholesky
[Termes IGN] image à très haute résolution
[Termes IGN] image RVBRésumé : (Auteur) Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors.
Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption.
To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes.
Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called “anisotropic Procrustes” and the “not-centered oblique Procrustes” algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition.
To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.Numéro de notice : A2016-793 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.05.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.05.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82510
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 485 - 495[article]Spatiotemporal subpixel mapping of time-series images / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
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Titre : Spatiotemporal subpixel mapping of time-series images Type de document : Article/Communication Auteurs : Qunming Wang, Auteur ; Wenzhong Shi, Auteur ; Peter M. Atkinson, Auteur Année de publication : 2016 Article en page(s) : pp 5397 - 5411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] détection de changement
[Termes IGN] image à très haute résolution
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] occupation du sol
[Termes IGN] précision des données
[Termes IGN] série temporelleRésumé : (Auteur) Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs. Numéro de notice : A2016-901 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2562178 En ligne : https://doi.org/10.1109/TGRS.2016.2562178 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83094
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5397 - 5411[article]Suivi spatiotemporel de la tache urbaine à l'aide de cartes anciennes, d'images satellitaires et de SIG. La cas de Blida en Algérie (de 1936 à 2015) / Elodie Ruch in Géomatique expert, n° 112 (septembre - octobre 2016)
PermalinkThe impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkTracking the seasonal dynamics of boreal forest photosynthesis using EO-1 hyperion reflectance : sensitivity to structural and illumination effects / Rocío Hernández-Clemente in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkTwo heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 2016)
PermalinkUse of a GPS-derived troposphere model to improve InSAR deformation estimates in the San Gabriel Valley, California / Nicolas Houlié in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkInvestigation of ionospheric effects on SAR Interferometry (InSAR): A case study of Hong Kong / Wu Zhu in Advances in space research, vol 58 n° 4 (August 2016)
PermalinkAtmospheric correction in time-series SAR interferometry for land surface deformation mapping : A case study of Taiyuan, China / Wei Tang in Advances in space research, vol 58 n° 3 (August 2016)
PermalinkBasal area and diameter distribution estimation using stereoscopic hemispherical images / Mariola Sánchez-González in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)
PermalinkDirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkQuantitative estimation and validation of the effects of the convergence, bisector elevation, and asymmetry angles on the positioning accuracies of satellite stereo pairs / Jaehoon Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)
PermalinkRadiometric correction of airborne radar images over forested terrain with topography / Marc Simard in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkSatellite image collection modeling for large area hazard emergency response / Shufan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 118 (August 2016)
PermalinkSea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study / Lei Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkSimultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing / Paris V. Giampouras in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkSoil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data / Lian He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkSpaceborne synthetic aperture radar data focusing on multicore-based architectures / Pasquale Imperatore in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
PermalinkAssessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India / Anju Bala in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
PermalinkAssessment of orthoimage and DEM derived from ZY-3 stereo image in Northeastern China / Y. Dong in Survey review, vol 48 n° 349 (July 2016)
PermalinkClassifying buildings from point clouds and images / Evangelos Maltezos in GIM international, vol 30 n° 7 (July 2016)
PermalinkEfficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkEstimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkFusion of LiDAR orthowaveforms and hyperspectral imagery for shallow river bathymetry and turbidity estimation / Zhigang Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkHigh fidelity / Penelope Richardson in GEO: Geoconnexion international, vol 15 n° 7 (July - August 2016)
PermalinkLearning-based superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkMapping and characterization of hydrological dynamics in coastal marsh using high temporal resolution Sentinel-1 images / Cécile Cazals in Remote sensing, vol 8 n° 7 (July 2016)
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PermalinkMultiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
PermalinkObject-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery / Mary Pyott Freeman in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)
PermalinkOptimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa / Romano Lottering in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
PermalinkPan-sharpening quality investigation of PLÉIADES-1A images / Mustafa Ozendi in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
PermalinkRecursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkRegistration-based mapping of aboveground disparities (RMAD) for building detection in off-nadir VHR stereo satellite imagery / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)
PermalinkRobust approach for recovery of rigorous sensor model using rational function model / Wen-chao Huang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkRPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)
PermalinkSelf-calibration of digital aerial camera using combined orthogonal models / Hadi Babapour in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)
PermalinkSparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkSpatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)
PermalinkA superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkUse of doppler parameters for ship velocity computation in SAR images / Alfredo Renga in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkSpectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)
PermalinkFusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
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PermalinkAccuracy Validation of Large-scale Block Adjustment without Control of ZY3 Images over China / Yang Bo in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-1 (July 2016)
PermalinkRefined satellite image orientation in the free open-source photogrammetric tools Apero/MicMac / Ewelina Rupnik in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-1 (July 2016)
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PermalinkAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)
PermalinkAn interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)
PermalinkAutomated bias-compensation approach for pushbroom sensor modeling using digital elevation model / Kwan-Young Oh in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
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