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Improving sensor fusion : a parametric method for the geometric coalignment of airborne hyperspectral and lidar data / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
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Titre : Improving sensor fusion : a parametric method for the geometric coalignment of airborne hyperspectral and lidar data Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Christian Rogass, Auteur ; Karl Segl, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3460 - 3474 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] alignement semi-dirigé
[Termes IGN] appariement géométrique
[Termes IGN] données lidar
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image multicapteur
[Termes IGN] points homologues
[Termes IGN] superposition d'images
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. Numéro de notice : A2016-855 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2518930 En ligne : https://doi.org/10.1109/TGRS.2016.2518930 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82994
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3460 - 3474[article]Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data / Elisa Schäfer in Ecological indicators, vol 64 (May 2016)
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Titre : Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data Type de document : Article/Communication Auteurs : Elisa Schäfer, Auteur ; Janne Heiskanen, Auteur ; Vuokko Heikinheimo, Auteur ; Petri Pellikka, Auteur Année de publication : 2016 Article en page(s) : pp 49 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre (flore)
[Termes IGN] biodiversité
[Termes IGN] canopée
[Termes IGN] forêt tropicale
[Termes IGN] image aérienne
[Termes IGN] Kenya
[Termes IGN] placette d'échantillonnage
[Termes IGN] segmentation d'imageRésumé : (auteur) With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data.
Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed.
According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species; r2 = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest.
In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be applied also for estimating beta diversity.Numéro de notice : A2016-346 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2015.12.026 En ligne : http://dx.doi.org/10.1016/j.ecolind.2015.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81035
in Ecological indicators > vol 64 (May 2016) . - pp 49 - 58[article]A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data / Gherardo Chirici in Remote sensing of environment, vol 176 (April 2016)
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Titre : A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data Type de document : Article/Communication Auteurs : Gherardo Chirici, Auteur ; Matteo Mura, Auteur ; Daniel McInerney, Auteur ; Nicolas Py , Auteur ; Erkki Tomppo, Auteur ; Lars T. Waser, Auteur ; Davide Travaglini, Auteur ; Ronald E. McRoberts, Auteur
Année de publication : 2016 Article en page(s) : pp 282 - 294 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] forêt
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] plus proche voisin, algorithme duRésumé : (auteur) The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta-analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN. Numéro de notice : A2016--196 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.02.001 Date de publication en ligne : 13/02/2016 En ligne : https://doi.org/10.1016/j.rse.2016.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91859
in Remote sensing of environment > vol 176 (April 2016) . - pp 282 - 294[article]Noise simulation and correction in synthetic airborne TIR Data for mineral quantification / Christoph Hecker in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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Titre : Noise simulation and correction in synthetic airborne TIR Data for mineral quantification Type de document : Article/Communication Auteurs : Christoph Hecker, Auteur ; Dean Riley, Auteur ; Mark Van Der Meijde, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1545 - 1553 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] erreur systématique
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] prospection minérale
[Termes IGN] quartz
[Termes IGN] rapport signal sur bruit
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] régression
[Termes IGN] simulationRésumé : (Auteur) Rock-forming minerals (such as feldspar and quartz) can be identified and quantified from thermal infrared (TIR) laboratory spectroscopy using spectral models. This paper uses synthetic airborne TIR spectra to test whether the hyperspectral Spatially Enhanced Broadband Array Spectrograph System (SEBASS) would theoretically be able to detect quartz and feldspar minerals and quantitatively predict mineral modes in felsic igneous rocks. Data from a previous laboratory study were used to simulate TIR spectra with band locations and noise levels of the SEBASS sensor. The quantitative partial least squares regression (PLSR) models from that study were applied to newly created synthetic SEBASS data, and results were compared with the predictions from the previous study. Predicted compositions based on SEBASS band positions are nearly identical (ρ = 0.995) to those based on laboratory resolution. Results are still reliable [prediction errors within 0.4% (absolute)] to the original laboratory PLSR predictions when adding up to 1% noise (about five times the SEBASS noise level) to the synthetic data. Prediction errors rapidly increase when noise levels beyond 1% are used. These results show that SEBASS' spectral resolution, spectral coverage, and signal-to-noise levels are sufficient to quantitatively predict quartz and feldspar amounts, and feldspar compositions with models based on PLSR. Spectral distortions, such as reduced spectral contrast, tilts, and vertical shifts, must be compensated for before these quantitative models are applied. A mean and standard deviation (MASD) normalization is proposed using a set of ground data for compensating systematic errors that are common to all image pixels. Numéro de notice : A2016-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2482386 En ligne : https://doi.org/10.1109/TGRS.2015.2482386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80005
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1545 - 1553[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible A robust digital watermarking algorithm for copyright protection of aerial photogrammetric images / Pai-Hui Hsu in Photogrammetric record, vol 31 n° 153 (March - May 2016)
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Titre : A robust digital watermarking algorithm for copyright protection of aerial photogrammetric images Type de document : Article/Communication Auteurs : Pai-Hui Hsu, Auteur ; Chih-Cheng Chen, Auteur Année de publication : 2016 Article en page(s) : pp 51 - 70 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] droit d'auteur
[Termes IGN] image aérienne
[Termes IGN] méthode robuste
[Termes IGN] tatouage numérique
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Current research on digital watermarking for the copyright protection of digital multimedia data has led to fairly advanced techniques with fruitful results. However, there remains a lack of research on digital watermarking for geospatial data, which is very costly to produce but is of great importance and with wide application. In this study an analysis and discussion of digital watermarking is carried out for digital aerial photogrammetric images. Focusing on the requirements for the main applications of such images, a feature-based digital watermarking algorithm is proposed. Testing and analysis of the robustness of the watermark is performed to achieve the goal of copyright protection, even after image processing and geometric transformation have been undertaken on the watermarked image. Furthermore, the image quality is (almost) preserved to avoid detrimental effects on subsequent applications. The experimental results prove that the proposed watermarking method has a certain degree of robustness and can resist most types of image-processing and geometric attacks, while maintaining the data quality of the aerial images. Numéro de notice : A2016-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12134 Date de publication en ligne : 29/02/2016 En ligne : https://doi.org/10.1111/phor.12134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80461
in Photogrammetric record > vol 31 n° 153 (March - May 2016) . - pp 51 - 70[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016011 RAB Revue Centre de documentation En réserve L003 Disponible Uniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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PermalinkSeamline determination for high resolution orthoimage mosaicking using watershed segmentation / Wang Mi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)
PermalinkCompressive sensing for multibaseline polarimetric SAR tomography of forested areas / Xinwu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)
PermalinkDevelopment of a SGM-based multi-view reconstruction framework for aerial imagery / Mathias Rothermel (2016)
PermalinkPermalinkMise en place de procédures automatiques en vue d’accélérer la production des plans topographiques au sein de l’entreprise Techni Drone / Kévin Javerliat (2016)
PermalinkStudy of lever-arm effect using embedded photogrammetry and on-board GPS receiver on UAV for metrological mapping purpose and proposal of a free ground measurements calibration procedure / Mehdi Daakir (2016)
PermalinkApplication of technical measures and software in constructing photorealistic 3D models of historical building using ground-based and aerial (UAV) digital images / Aleksander Zarnowski in Reports on geodesy and geoinformatics, vol 99 (December 2015)
PermalinkReal-time atmospheric correction of AVIRIS-NG imagery / Brian D. Bue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)
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