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A 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)
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Titre : A tensor decomposition-based anomaly detection algorithm for hyperspectral image Type de document : Article/Communication Auteurs : Xing Zhang, Auteur ; Gongjian Wen, Auteur ; Wei Dai, Auteur Année de publication : 2016 Article en page(s) : pp 5801 - 5820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] décomposition
[Termes IGN] détection d'anomalie
[Termes IGN] image hyperspectrale
[Termes IGN] signature spectrale
[Termes IGN] tenseurRésumé : (auteur) Anomalies usually refer to targets with a spot of pixels (even subpixels) that stand out from their neighboring background clutter pixels in hyperspectral imagery (HSI). Compared to backgrounds, anomalies have two main characteristics. One is the spectral anomaly, i.e., their spectral signatures are different from those associated to their surrounding backgrounds; another is the spatial anomaly, i.e., anomalies occur as few pixels (even subpixels) embedded in the local homogeneous backgrounds. However, most of the existing anomaly detection algorithms for HSI only employed the spectral anomaly. If the two characteristics are exploited in a detection method simultaneously, better performance may be achieved. The third-order (two modes for space and one mode for spectra) tensor representation of HSI has been proved to be an effective tool to describe the spatial and spectral information equivalently; therefore, tensor representation is convenient for exhibiting the two characteristics of anomalies simultaneously. In this paper, a new anomaly detection method based on tensor decomposition is proposed and divided into three steps. Three factor matrices and a core tensor are first estimated from the third-order tensor that is constructed from the HSI data cube by using the Tucker decomposition, and their major and minor principal components (PCs) are more likely to correspond to the spectral signatures of the backgrounds and the anomalies, respectively. In the second step, a reconstruction-error-based method is presented to find the first largest PCs along each mode to eliminate the spectral signatures of the backgrounds as much as possible, and thus, the remaining data may be modeled as the spectral signatures of the anomalies with a Gaussian noise. Finally, a CFAR test is implemented to detect the anomalies from the remaining data. Experiments with simulated, synthetic, and real HSI data sets reveal that the proposed method outperforms those spectral-anomaly-based methods with better detection probability and less false alarm rate. Numéro de notice : A2016-862 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2572400 En ligne : https://doi.org/10.1109/TGRS.2016.2572400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82894
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5801 - 5820[article]Automatic rough georeferencing of multiview oblique and vertical aerial image datasets of urban scenes / Styliani Verykokou in Photogrammetric record, vol 31 n° 155 (September - November 2016)
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Titre : Automatic rough georeferencing of multiview oblique and vertical aerial image datasets of urban scenes Type de document : Article/Communication Auteurs : Styliani Verykokou, Auteur ; Charalabos Ioannnidis, Auteur Année de publication : 2016 Article en page(s) : pp 281 - 303 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] automatisation
[Termes IGN] calage
[Termes IGN] géoréférencement
[Termes IGN] image aérienne à axe vertical
[Termes IGN] image aérienne oblique
[Termes IGN] scène urbaine
[Termes IGN] système de référence géodésique
[Termes IGN] visée oblique
[Termes IGN] visée verticaleRésumé : (Auteur) Multi-perspective airborne images that combine oblique and vertical views of the ground have proved to be a valuable source of information for numerous applications requiring a digital representation of the world. In this paper, an automatic methodology for rough georeferencing of large datasets of multiview oblique and vertical aerial images of urban regions without any metadata is proposed. Using feature-based matching combined with robust model fitting and least-squares techniques, the method requires the measurement of a minimum number of points with known coordinates in only one image. The results of this methodology are discussed through the presentation of a developed software suite which identifies the overlapping images, georeferences them, extracts their footprints, subdivides the images into groups based on these footprints and detects the images that cover a specific region. Numéro de notice : A2016-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12156 Date de publication en ligne : 18/09/2016 En ligne : https://doi.org/10.1111/phor.12156 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82246
in Photogrammetric record > vol 31 n° 155 (September - November 2016) . - pp 281 - 303[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016031 RAB Revue Centre de documentation En réserve L003 Disponible Blending zone determination for aerial orthimage mosaicking / Chao-Hung Lin in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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Titre : Blending zone determination for aerial orthimage mosaicking Type de document : Article/Communication Auteurs : Chao-Hung Lin, Auteur ; Bo-Heng Chen, Auteur ; Bo-Yi Lin, Auteur ; Han-Szu Chou, Auteur Année de publication : 2016 Article en page(s) : pp 426 - 436 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Orthophotographie, orthoimage
[Termes IGN] aberration chromatique
[Termes IGN] aberration instrumentale
[Termes IGN] lissage de valeur
[Termes IGN] mosaïquage d'images
[Termes IGN] orthophotoplan numérique
[Termes IGN] raccord d'images
[Termes IGN] similitude spectrale
[Termes IGN] valeur radiométrique
[Termes IGN] zone tamponRésumé : (Auteur) Creating a composed image from a set of aerial images is a fundamental step in orthomosaic generation. One of the processes involved in this technique is determining an optimal seamline in an overlapping region to stitch image patches seamlessly. Most previous studies have solved this optimization problem by searching for a one-pixel-wide seamline with an objective function. This strategy significantly reduced pixel mismatches on the seamline caused by geometric distortions of images but did not fully consider color discontinuity and mismatch problems that occur around the seamline, which sometimes cause mosaicking artifacts. This study proposes a blending zone determination scheme with a novel path finding algorithm to reduce the occurrence of unwanted artifacts. Instead of searching for a one-pixel-wide seamline, a blending zone, which is a k-pixel-wide seamline that passes through high-similarity pixels in the overlapping region, is determined using a hierarchical structure. This strategy allows for not only seamless stitching but also smooth color blending of neighboring image patches. Moreover, the proposed method searches for a blending zone without the pre-process of highly mismatched pixel removal and additional geographic data of road vectors and digital surface/elevation models, which increases the usability of the approach. Qualitative and quantitative analyses of aerial images demonstrate the superiority of the proposed method to related methods in terms of avoidance of passing highly mismatched pixels. Numéro de notice : A2016-791 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.07.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82508
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 426 - 436[article]Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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Titre : Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery Type de document : Article/Communication Auteurs : S. Stagakis, Auteur ; Theofilos Vanikiotis, Auteur ; Olga Sykioti, Auteur Année de publication : 2016 Article en page(s) : pp 79 - 89 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des mélanges spectraux
[Termes IGN] carte de la végétation
[Termes IGN] classification bayesienne
[Termes IGN] effet d'ombre
[Termes IGN] espèce végétale
[Termes IGN] Fagus sylvatica
[Termes IGN] Grèce
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image PROBA-CHRIS
[Termes IGN] orthoimage
[Termes IGN] parc naturel national
[Termes IGN] partition d'image
[Termes IGN] Pinus nigra
[Termes IGN] richesse floristique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The advancing technology of hyperspectral remote sensing offers the opportunity of accurate land cover characterization of complex natural environments. In this study, a linear spectral unmixing algorithm that incorporates a novel hierarchical Bayesian approach (BI-ICE) was applied on two spatially and temporally adjacent CHRIS/PROBA images over a forest in North Pindos National Park (Epirus, Greece). The scope is to investigate the potential of this algorithm to discriminate two different forest species (i.e. beech – Fagus sylvatica, pine – Pinus nigra) and produce accurate species-specific abundance maps. The unmixing results were evaluated in uniformly distributed plots across the test site using measured fractions of each species derived by very high resolution aerial orthophotos. Landsat-8 images were also used to produce a conventional discrete-type classification map of the test site. This map was used to define the exact borders of the test site and compare the thematic information of the two mapping approaches (discrete vs abundance mapping). The required ground truth information, regarding training and validation of the applied mapping methodologies, was collected during a field campaign across the study site. Abundance estimates reached very good overall accuracy (R2 = 0.98, RMSE = 0.06). The most significant source of error in our results was due to the shadowing effects that were very intense in some areas of the test site due to the low solar elevation during CHRIS acquisitions. It is also demonstrated that the two mapping approaches are in accordance across pure and dense forest areas, but the conventional classification map fails to describe the natural spatial gradients of each species and the actual species mixture across the test site. Overall, the BI-ICE algorithm presented increased potential to unmix challenging objects with high spectral similarity, such as different vegetation species, under real and not optimum acquisition conditions. Its full potential remains to be investigated in further and more complex study sites in view of the upcoming satellite hyperspectral missions. Numéro de notice : A2016-778 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.05.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.05.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82473
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 79 - 89[article]Geometric calibration of a hyperspectral frame camera / Raquel A. de Oliveira in Photogrammetric record, vol 31 n° 155 (September - November 2016)
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Titre : Geometric calibration of a hyperspectral frame camera Type de document : Article/Communication Auteurs : Raquel A. de Oliveira, Auteur ; Antonio Maria Garcia Tommaselli, Auteur ; Eija Honkavaara, Auteur Année de publication : 2016 Article en page(s) : pp 325 - 347 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] étalonnage géométrique
[Termes IGN] filtre adaptatif
[Termes IGN] image hyperspectrale
[Termes IGN] interféromètreRésumé : (Auteur) Recently, miniaturised hyperspectral sensors operable from small unmanned airborne vehicle platforms have entered the market. The emerging hyperspectral imaging technologies, based on frame cameras and tuneable filters, are attractive alternatives to hyperspectral pushbroom sensors. This paper addresses the geometric calibration process of a hyperspectral frame camera based on a Fabry–Perot interferometer. However, the addition of more optical elements in front of the image sensor can affect the parameters related to the internal geometry of the camera, and a deficiency in knowledge regarding these parameters can have a critical effect on the accuracy of 3D measurements in photogrammetric applications. The experiments focused on assessing the self-calibrating bundle adjustment to verify the behaviour of the interior parameters, considering different spectral bands. The results indicated that the applied self-calibration method can accurately characterise the interior parameters of this camera and that one set of parameters is required for each internal sensor. Numéro de notice : A2016-724 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12153 Date de publication en ligne : 24/08/2016 En ligne : https://doi.org/10.1111/phor.12153 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82256
in Photogrammetric record > vol 31 n° 155 (September - November 2016) . - pp 325 - 347[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016031 RAB Revue Centre de documentation En réserve L003 Disponible Mapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkRetrieval 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)
PermalinkSatellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkSemiblind 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)
PermalinkShadow 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)
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)
PermalinkTwo heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 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)
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)
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)
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)
PermalinkRecursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li 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)
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)
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)
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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PermalinkAn interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)
PermalinkCorrection of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
PermalinkImproving 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)
PermalinkA manifold alignment approach for hyperspectral image visualization with natural color / Danping Liao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkThermal infrared reveals vegetation stress / Thomas A. Groen in GIM international, vol 30 n° 6 (June 2016)
PermalinkVector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkPermalinkKernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkRemote sensing of alpine glaciers in visible and infrared wavelengths: a survey of advances and prospects / Anshuman Bhardwaj in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)
PermalinkRemote sensing technologies for enhancing forest inventories: A review / Joanne C. White in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])
PermalinkUnsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkForest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)
PermalinkComparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
PermalinkComparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
PermalinkNoise 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)
PermalinkA penalized spline-based attitude model for high-resolution satellite imagery / Hongbo Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
PermalinkMatrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
PermalinkTelespazio aurait-il trouvé la solution pour développer l'usage du spatial / Françoise de Blomac in DécryptaGéo le mag, n° 174 (février 2016)
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