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Development of a network-based method for unmixing of hyperspectral data / V. Karathanassi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 3 (March 2012) . - pp 839 - 849 Titre : Development of a network-based method for unmixing of hyperspectral data Type de document : Article/Communication Auteurs : V. Karathanassi, Auteur ; D. Sykas, Auteur ; Konstantinos Topouzelis, Auteur Année de publication : 2012 Article en page(s) : pp 839 - 849 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] distance euclidienne [Termes IGN] image hyperspectrale Résumé : (Auteur) This paper presents a new nonlinear unmixing method. Based on relative distances which imply nonlinearity, the method introduces the “fractional distance” as a key variable that quantifies interactions between pixels and endmembers. Relationships between fractional distances and abundance fractions are built through networks. Because an equal spectral mixture of ground spectral classes present on the surface sensed is likely impossible, the proposed method, due to its mathematical concept, reveals unknown endmembers. Three versions of the method have been developed: the nonconstrained, the sum-to-one, and the fully constrained versions. Evaluation of the method using synthetic and real data showed that the method is robust with clear and interpretable results and provides reliable abundance fractions, particularly the sum-to-one and the fully constrained versions of the method. The new unmixing method has also been compared with the fully constrained least squares method. Numéro de notice : A2012-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2163412 Date de publication en ligne : 15/09/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2163412 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31547 [article]

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Hyperspectral unmixing based on mixtures of Dirichlet components / J. Nascimento in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 3 (March 2012) . - pp 863 - 878 Titre : Hyperspectral unmixing based on mixtures of Dirichlet components Type de document : Article/Communication Auteurs : J. Nascimento, Auteur ; José Bioucas-Dias, Auteur Année de publication : 2012 Article en page(s) : pp 863 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification non dirigée [Termes IGN] données lidar [Termes IGN] image hyperspectrale [Termes IGN] problème de Dirichlet Résumé : (Auteur) This paper introduces a new unsupervised hyperspectral unmixing method conceived to linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume, usually estimated by the purely geometrically based algorithms, is far way from the true simplex associated with the endmembers. The proposed method, an extension of our previous studies, resorts to the statistical framework. The abundance fraction prior is a mixture of Dirichlet densities, thus automatically enforcing the constraints on the abundance fractions imposed by the acquisition process, namely, nonnegativity and sum-to-one. A cyclic minimization algorithm is developed where the following are observed: 1) The number of Dirichlet modes is inferred based on the minimum description length principle; 2) a generalized expectation maximization algorithm is derived to infer the model parameters; and 3) a sequence of augmented Lagrangian-based optimizations is used to compute the signatures of the endmembers. Experiments on simulated and real data are presented to show the effectiveness of the proposed algorithm in unmixing problems beyond the reach of the geometrically based state-of-the-art competitors. Numéro de notice : A2012-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2163941 En ligne : https://doi.org/10.1109/TGRS.2011.2163941 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31548 [article]

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Extraction of building roof contours from LiDAR data using a Markov-random-field-based approach / E. Dos Santos Galvanin in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 3 (March 2012) . - pp 981 - 987 Titre : Extraction of building roof contours from LiDAR data using a Markov-random-field-based approach Type de document : Article/Communication Auteurs : E. Dos Santos Galvanin, Auteur ; A. Dal Poz, Auteur Année de publication : 2012 Article en page(s) : pp 981 - 987 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme du recuit simulé [Termes IGN] bati [Termes IGN] champ aléatoire de Markov [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] toit Résumé : (Auteur) This paper proposes a method for the automatic extraction of building roof contours from a digital surface model (DSM) by regularizing light detection and ranging (LiDAR) data. The method uses two steps. First, to detect aboveground objects (buildings, trees, etc.), the DSM is segmented through a recursive splitting technique followed by a region-merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The optimal configuration of building roof contours is found by minimizing the energy function using a simulated annealing algorithm. Experiments carried out with the LiDAR-based DSM show that the proposed method works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives. Numéro de notice : A2012-101 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2163823 Date de publication en ligne : 15/09/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2163823 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31549 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012031	RAB	Revue	Centre de documentation	En réserve L003	Disponible