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Les journées de la recherche 2015 à l'IGN / Anonyme in Géomatique expert, n° 103 (mars - avril 2015)
[article]
Titre : Les journées de la recherche 2015 à l'IGN Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2015 Article en page(s) : pp 4 - 9 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] acquisition d'images
[Termes IGN] agriculture de précision
[Termes IGN] drone
[Termes IGN] image hyperspectrale
[Termes IGN] sylvicultureRésumé : (Editeur) Comme chaque année à la fin de l'hiver, l'IGN organisait ses "Journées de la recherche" pour donner la parole aux chercheurs des différents laboratoires afin qu'ils exposent le but et l'état de leurs travaux. Numéro de notice : A2015-125 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75779
in Géomatique expert > n° 103 (mars - avril 2015) . - pp 4 - 9[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 265-2015021 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001689 GEO Revue Nogent-sur-Vernisson Salle périodiques Disponible Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization / Zhangyang Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization Type de document : Article/Communication Auteurs : Zhangyang Wang, Auteur ; Nasser M. Nasrabadi, Auteur ; Thomas S. Huang, Auteur Année de publication : 2015 Article en page(s) : pp 1161 - 1173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectraleRésumé : (Auteur) We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results. Numéro de notice : A2015-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2335177 Date de publication en ligne : 30/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2335177 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75792
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1161 - 1173[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Supervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Supervised spectral–spatial hyperspectral image classification with weighted markov random fields Type de document : Article/Communication Auteurs : Le Sun, Auteur ; Zebin Wu, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] pondération
[Termes IGN] régression logistiqueRésumé : (Auteur) This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic. Numéro de notice : A2015-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2344442 Date de publication en ligne : 18/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2344442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75800
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1490 - 1503[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
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Titre : Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information Type de document : Article/Communication Auteurs : Janja Avbelj, Auteur ; Dorota Iwaszczuk, Auteur ; Rupert Müller, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 23 - 34 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] appariement d'images
[Termes IGN] classification orientée objet
[Termes IGN] détection du bâti
[Termes IGN] données multicapteurs
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] superposition d'imagesRésumé : (auteur) For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches. Numéro de notice : A2015-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.05.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.05.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75237
in ISPRS Journal of photogrammetry and remote sensing > vol 100 (February 2015) . - pp 23 - 34[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
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Titre : Gabor feature-based collaborative representation for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Linlin Shen, Auteur ; Qingquan Li, Auteur Année de publication : 2015 Article en page(s) : pp 1118 - 1129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification spectrale
[Termes IGN] conception collaborative
[Termes IGN] état de l'art
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, which has successfully been applied to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery (HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of both the classifier complexity and generalization ability from very small training sets. Numéro de notice : A2015-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2334608 En ligne : 10.1109/TGRS.2014.2334608 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75624
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 1118 - 1129[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible vol 100 - February 2015 - High-resolution Earth imaging for geospatial information (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Christian HeipkePermalinkHyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkSparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkAn abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkExtended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkExterior orientation of hyperspectral frame images collected with UAV for forest applications / Adilson Berveglieri (2015)PermalinkExtraction of optimal spectral bands using hierarchical band merging out of hyperspectral data / Arnaud Le Bris (2015)PermalinkHierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkHyperspectral image denoising via sparse representation and low-rank constraint / Yong-Qiang Zhao in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)Permalink