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Close-range photogrammetric tools for epigraphic surveys / Mariam Samaan in Journal on Computing and Cultural Heritage, JOCCH, vol 9 n° 3 (November 2016)
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Titre : Close-range photogrammetric tools for epigraphic surveys Type de document : Article/Communication Auteurs : Mariam Samaan , Auteur ; Marc Pierrot-Deseilligny
, Auteur ; Raphaële Heno
, Auteur ; Etienne de La Vaissière, Auteur ; Jacques Roger, Auteur
Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données localisées 3D
[Termes IGN] hiéroglyphe
[Termes IGN] image numérique
[Termes IGN] MicMac
[Termes IGN] monument
[Termes IGN] semis de points
[Termes IGN] site archéologiqueRésumé : (auteur) In this article, we present a method to carry out a computerized epigraphic survey of historic stones and their engraved epigraphy. In fact, the preservation of archaeological objects has always been an issue of concern for the research community. On the one hand, the fragility of the objects limits their study. On the other hand, such objects are housed in museums, libraries, and institutions worldwide, locations that significantly limit their accessibility. Different survey methods have been carried out in these attempts to overcome the aforementioned limitations. Among them, the MicMac open source software was used in this work to survey and process the dense correlation of the hieroglyphics inscriptions engraved on archaeological stones. Relatively standard workflows have led to depth maps, which can be represented either as 3D point clouds or shaded relief images. This article shows the application of this method in two epigraphic survey case studies. Numéro de notice : A2016--171 Affiliation des auteurs : ENSG+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/2966985 Date de publication en ligne : 03/11/2016 En ligne : https://hal.science/halshs-02954556v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90821
in Journal on Computing and Cultural Heritage, JOCCH > vol 9 n° 3 (November 2016)[article]Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection Type de document : Article/Communication Auteurs : Jie Feng, Auteur ; Licheng Jiao, Auteur ; Tao Sun, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6516 - 6530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectrale
[Termes IGN] intelligence artificielle
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency. Numéro de notice : A2016-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585961 En ligne : https://doi.org/10.1109/TGRS.2016.2585961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83140
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6516 - 6530[article]Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
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Titre : Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification Type de document : Article/Communication Auteurs : Zhi He, Auteur ; Lin Liu, Auteur Année de publication : 2016 Article en page(s) : pp 11 – 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] module d'extensionRésumé : (Auteur) Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2l1,2-norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods. Numéro de notice : A2016--011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.08.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83873
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 11 – 27[article]Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
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Titre : Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach Type de document : Article/Communication Auteurs : Michał Romaszewski, Auteur ; Przemysław Głomb, Auteur ; Michał Cholewa, Auteur Année de publication : 2016 Article en page(s) : pp 60 – 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification automatique
[Termes IGN] détection de cible
[Termes IGN] données localisées
[Termes IGN] image hyperspectrale
[Termes IGN] performanceRésumé : (Auteur) We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent ‘experts’ (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm’s performance on several publicly available hyperspectral data sets. Numéro de notice : A2016--015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83877
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 60 – 76[article]Wave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Wave period and coastal bathymetry using wave propagation on optical images Type de document : Article/Communication Auteurs : Céline Danilo, Auteur ; Farid Melgani, Auteur Année de publication : 2016 Article en page(s) : pp 6307 - 6319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie
[Termes IGN] fréquence
[Termes IGN] Hawaii (Etats-Unis)
[Termes IGN] image Landsat-8
[Termes IGN] image optique
[Termes IGN] lever bathymétrique
[Termes IGN] littoral
[Termes IGN] rayonnement électromagnétique
[Termes IGN] vagueRésumé : (Auteur) We propose a method based on combining wave tracing and linear wave theory for the estimation of wave period and bathymetry in coastal areas from satellite images. The method depends on several parameters for which we provide ranges of variations adapted to the instrument. Experimental results are conducted on several sites located around the Hawaiian island of Oahu, using 13 Landsat-8 images. Results show that wave period estimations are compatible with the wave buoy measurements in all cases. In addition, bathymetry estimation results show a standard deviation of less than 15% of the observed depth out of the surf zone until 20 m for sites with a direct exposure to the swell and with an absence of clouds. The proposed method, which does not rely on ancillary data, represents a promising tool for bathymetry estimation using satellite images in which waves are present. Numéro de notice : A2016-912 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2579266 En ligne : https://doi.org/10.1109/TGRS.2016.2579266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83134
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6307 - 6319[article]An operational high-resolution forest inventory / Julianno Sambatti in GIM international, vol 30 n° 10 (October 2016)
PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkDevelopment of a large-format UAS imaging system with the construction of a one sensor geometry from a multicamera array / Jiann-Yeou Rau in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)
PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkHabitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)
PermalinkInfluence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)
PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)
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