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Titre : Linear Algebra : A Course for Physicists and Engineers Type de document : Monographie Auteurs : Arak M. Mathai, Auteur ; Hans J. Haubold, Auteur Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2017 Importance : 450 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-056235-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Algèbre
[Termes IGN] algèbre linéaire
[Termes IGN] arc
[Termes IGN] équation différentielle
[Termes IGN] espace vectoriel
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] statistique mathématiqueRésumé : (éditeur) In order not to intimidate students by a too abstract approach, this textbook on linear algebra is written to be easy to digest by non-mathematicians. It introduces the concepts of vector spaces and mappings between them without dwelling on statements such as theorems and proofs too much. It is also designed to be self-contained, so no other material is required for an understanding of the topics covered. As the basis for courses on space and atmospheric science, remote sensing, geographic information systems, meteorology, climate and satellite communications at UN-affiliated regional centers, various applications of the formal theory are discussed as well. These include differential equations, statistics, optimization and some engineering-motivated problems in physics. Note de contenu : 1- Vectors
2- Matrices
3- Determinants
4- Eigenvalues and eigenvectors
5- Somme applications of matrices and determinants
6- Matrix series and additional properties of matricesNuméro de notice : 25804 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie DOI : 10.1515/9783110562507 En ligne : https://doi.org/10.1515/9783110562507 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96609 The weight matrix determination of systematic bias calibration for a laser altimeter / Ma Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)
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Titre : The weight matrix determination of systematic bias calibration for a laser altimeter Type de document : Article/Communication Auteurs : Ma Yue, Auteur ; Li Song, Auteur ; Lu Xiushan, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 847 - 852 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données ICEsat
[Termes IGN] erreur de mesure
[Termes IGN] étalonnage
[Termes IGN] géolocalisation
[Termes IGN] incertitude de mesurage
[Termes IGN] matrice
[Termes IGN] matrice d'erreurRésumé : (Auteur) The geolocation accuracy of satellite laser altimeters is significantly influenced by on-orbit misalignment and ranging biases. Few researchers have investigated the weight matrix determination method, which plays a critical role in bias estimation. In this article, a systematic misalignment and ranging bias model was deduced. Based on the least squares criterion, a bias calibration method was designed for use with solid natural surfaces; and the weight matrix was defined according to the ranging uncertainty theory. Referring to the Geoscience Laser Altimeter System (glas) parameters, the established model and method were verified using programming simulations, which indicated with a misalignment of tens of arc-seconds in the pitch and roll directions and a ranging bias of several centimeters, by using the weight matrix, the estimation accuracies of the misalignment and ranging bias increased by 0.22 and 2 cm, respectively. Consequently, the geolocation accuracy increased by approximately 0.64 m horizontally and 3 cm vertically for a 1° sloping surface. Numéro de notice : A2016-944 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.14358/PERS.82.11.847 En ligne : http://dx.doi.org/10.14358/PERS.82.11.847 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83436
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 11 (November 2016) . - pp 847 - 852[article]Taking correlations in GPS least squares adjustments into account with a diagonal covariance matrix / Gaël Kermarrec in Journal of geodesy, vol 90 n° 9 (September 2016)
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Titre : Taking correlations in GPS least squares adjustments into account with a diagonal covariance matrix Type de document : Article/Communication Auteurs : Gaël Kermarrec, Auteur ; Steffen Schön, Auteur Année de publication : 2016 Article en page(s) : pp 793 – 805 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] compensation par moindres carrés
[Termes IGN] corrélation
[Termes IGN] données GPS
[Termes IGN] estimateur
[Termes IGN] matrice de covariance
[Termes IGN] matrice diagonale
[Termes IGN] pondération
[Termes IGN] positionnement différentiel
[Termes IGN] positionnement par GPS
[Termes IGN] régression
[Termes IGN] série temporelle
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) Based on the results of Luati and Proietti (Ann Inst Stat Math 63:673–686, 2011) on an equivalence for a certain class of polynomial regressions between the diagonally weighted least squares (DWLS) and the generalized least squares (GLS) estimator, an alternative way to take correlations into account thanks to a diagonal covariance matrix is presented. The equivalent covariance matrix is much easier to compute than a diagonalization of the covariance matrix via eigenvalue decomposition which also implies a change of the least squares equations. This condensed matrix, for use in the least squares adjustment, can be seen as a diagonal or reduced version of the original matrix, its elements being simply the sums of the rows elements of the weighting matrix. The least squares results obtained with the equivalent diagonal matrices and those given by the fully populated covariance matrix are mathematically strictly equivalent for the mean estimator in terms of estimate and its a priori cofactor matrix. It is shown that this equivalence can be empirically extended to further classes of design matrices such as those used in GPS positioning (single point positioning, precise point positioning or relative positioning with double differences). Applying this new model to simulated time series of correlated observations, a significant reduction of the coordinate differences compared with the solutions computed with the commonly used diagonal elevation-dependent model was reached for the GPS relative positioning with double differences, single point positioning as well as precise point positioning cases. The estimate differences between the equivalent and classical model with fully populated covariance matrix were below the mm for all simulated GPS cases and below the sub-mm for the relative positioning with double differences. These results were confirmed by analyzing real data. Consequently, the equivalent diagonal covariance matrices, compared with the often used elevation-dependent diagonal covariance matrix is appropriate to take correlations in GPS least squares adjustment into account, yielding more accurate cofactor matrices of the unknown. Numéro de notice : A2016-654 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0911-z En ligne : http://dx.doi.org/10.1007/s00190-016-0911-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81856
in Journal of geodesy > vol 90 n° 9 (September 2016) . - pp 793 – 805[article]Simultaneously 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)
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Titre : Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing Type de document : Article/Communication Auteurs : Paris V. Giampouras, Auteur ; Konstantinos E. Themelis, Auteur ; Athanasios A. Rontogiannis, Auteur ; Konstantinos D. Koutroumbas, Auteur Année de publication : 2016 Article en page(s) : pp 4775 - 4789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuseRésumé : (Auteur) In a plethora of applications dealing with inverse problems, e.g., image processing, social networks, compressive sensing, and biological data processing, the signal of interest is known to be structured in several ways at the same time. This premise has recently guided research into the innovative and meaningful idea of imposing multiple constraints on the unknown parameters involved in the problem under study. For instance, when dealing with problems whose unknown parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low rankness is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced in an attempt to exploit both spatial correlation and sparse representation of pixels lying in the homogeneous regions of hyperspectral images. To this end, a novel mixed penalty term is first defined consisting of the sum of the weighted ℓ1 and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an incremental proximal sparse and low-rank unmixing algorithm; and 2) an algorithm based on the alternating direction method of multipliers. The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data. Numéro de notice : A2016-891 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2551327 En ligne : https://doi.org/10.1109/TGRS.2016.2551327 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83071
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4775 - 4789[article]Sparse output coding for scalable visual recognition / Bin Zhao in International journal of computer vision, vol 119 n° 1 (August 2016)
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Titre : Sparse output coding for scalable visual recognition Type de document : Article/Communication Auteurs : Bin Zhao, Auteur ; Eric P. Xing, Auteur Année de publication : 2016 Article en page(s) : pp 60 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] décodage
[Termes IGN] matrice
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach. Numéro de notice : A2016--152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-015-0839-4 En ligne : https://doi.org/10.1007/s11263-015-0839-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85920
in International journal of computer vision > vol 119 n° 1 (August 2016) . - pp 60 - 75[article]Matrix-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)PermalinkPermalinkForcing scale invariance in multipolarization SAR change detection / Vincenzo Carotenuto in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkGéomatique, modèles numériques de terrain / Patrick Julien (2016)PermalinkPermalinkOn estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)PermalinkPermalinkTotal-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkGIS based drainage morphometry and its influence on hydrology in parts of Western Ghats region, Maharashtra, India / Dipak R. Samal in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkSubstance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)PermalinkAdaptive relative motion representation of space–time trajectories / Antoni B. Moore in Cartographic journal (the), Vol 52 n° 2 (May 2015)PermalinkInterferometric phase image estimation via sparse coding in the complex domain / Hao Hongxing in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkCollaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkProgressive band processing of constrained energy minimization for subpixel detection / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 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)PermalinkPermalinkGeneralizations of bounds on the index of convergence to weighted digraphs / Glenn Merlet in Discrete Applied Mathematics, vol 178 ([11/12/2014])PermalinkAssociation-matrix-based sample consensus approach for automated registration of terrestrial laser scans using linear features / Kaleel Al-Durgham in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)PermalinkAutomated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)Permalink