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Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1703 - 1717 Titre : Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Yongwan Chun, Auteur Année de publication : 2019 Article en page(s) : pp 1703 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approximation [Termes IGN] autocorrélation spatiale [Termes IGN] filtrage numérique d'image [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-TM [Termes IGN] régression linéaire [Termes IGN] segmentation d'image [Termes IGN] tessellation [Termes IGN] vecteur propre Mots-clés libres : Moran eigenvector spatial filtering Résumé : (auteur) Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. Numéro de notice : A2019-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1080/13658816.2019.1593421 Date de publication en ligne : 02/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1593421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93479 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2019092	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Testing spatial heterogeneity in geographically weighted principal components analysis / Javier Roca-Pardiñas in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 676 - 693 Titre : Testing spatial heterogeneity in geographically weighted principal components analysis Type de document : Article/Communication Auteurs : Javier Roca-Pardiñas, Auteur ; Celestino Ordóñez, Auteur ; Tomás R. Cotos-Yáñez, Auteur ; Rubén Pérez-Álvarez, Auteur Année de publication : 2017 Article en page(s) : pp 676 - 693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse en composantes principales [Termes IGN] données localisées [Termes IGN] hétérogénéité spatiale [Termes IGN] interpolation par pondération de zones [Termes IGN] vecteur propre Résumé : (Auteur) We propose a method to evaluate the existence of spatial variability in the covariance structure in a geographically weighted principal components analysis (GWPCA). The method, that is extensive to locally weighted principal components analysis, is based on performing a statistical hypothesis test using the eigenvectors of the PCA scores covariance matrix. The application of the method to simulated data shows that it has a greater statistical power than the current statistical test that uses the eigenvalues of the raw data covariance matrix. Finally, the method was applied to a real problem whose objective is to find spatial distribution patterns in a set of soil pollutants. The results show the utility of GWPCA versus PCA. Numéro de notice : A2017-079 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1224886 En ligne : http://dx.doi.org/10.1080/13658816.2016.1224886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84342 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Vector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3208 - 3220 Titre : Vector attribute profiles for hyperspectral image classification Type de document : Article/Communication Auteurs : Erchan Aptoula, Auteur ; Mauro Dalla Mura, Auteur ; Sébastien Lefèvre, Auteur Année de publication : 2016 Article en page(s) : pp 3208 - 3220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification automatique [Termes IGN] image hyperspectrale [Termes IGN] morphologie mathématique [Termes IGN] niveau de gris (image) [Termes IGN] vecteur propre [Termes IGN] végétation Résumé : (Auteur) Morphological attribute profiles are among the most prominent spectral-spatial pixel description methods. They are efficient, effective, and highly customizable multiscale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general and hyperspectral images in particular has been so far conducted using the marginal strategy, i.e., by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector-ordering relation that leads to the computation of a single max and min tree per hyperspectral data set, from which attribute profiles can then be computed as usual. We explore known vector-ordering relations for constructing such max trees and, subsequently, vector attribute profiles and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common data sets, where the proposed approach outperforms the widely used marginal strategy. Numéro de notice : A2016-850 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2513424 En ligne : https://doi.org/10.1109/TGRS.2015.2513424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82932 [article]

Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis / Yunzhong Shen in Journal of geodesy, vol 88 n° 1 (January 2014)  

Public [article]  inJournal of geodesy > vol 88 n° 1 (January 2014) . - pp 1 - 12 Titre : Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis Type de document : Article/Communication Auteurs : Yunzhong Shen, Auteur ; Weiwei Li, Auteur ; Guochang Xu, Auteur ; Bofeng Li, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse en composantes principales [Termes IGN] déformation de la croute terrestre [Termes IGN] filtrage spatiotemporel [Termes IGN] positionnement par GNSS [Termes IGN] série temporelle [Termes IGN] vecteur propre Résumé : (Auteur) The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405–3421, 2006) in case of no missing data in the time series and to the extended ‘stacking’ approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999–2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data. Numéro de notice : A2014-100 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-013-0663-y Date de publication en ligne : 18/10/2013 En ligne : https://doi.org/10.1007/s00190-013-0663-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33005 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
266-2014011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Denoising atmospheric radar signals using spectral-based subspace method applicable for PBS wind estimation / V.N. Sureshbabu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3853 - 3861 Titre : Denoising atmospheric radar signals using spectral-based subspace method applicable for PBS wind estimation Type de document : Article/Communication Auteurs : V.N. Sureshbabu, Auteur ; V.K. Anandan, Auteur ; Toshitaka Tsuda, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 3853 - 3861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] décomposition spectrale [Termes IGN] écho radar [Termes IGN] filtrage du bruit [Termes IGN] image radar [Termes IGN] sous-espace [Termes IGN] vecteur propre Résumé : (Auteur) This paper mainly focuses on the advantages of subspace-based eigenvector (EV) spectral estimator to improve the power spectrum and the quality of calculations in spectrum parameter estimation. In general, the spectrum produced by most of subspace methods is sharply peaked at the frequency of complex sinusoids. Although subspace methods exhibit the advantage of spectral resolution, the retrieval of the actual spectrum width is not well observed in many cases, compared with standard Fourier estimates. Several simulation works are carried out to determine the unknown order of the signal correlation matrix, which significantly helps in obtaining the equivalent Fourier spectrum using EV along with numerous advantages of the subspace method for better estimation of spectrum parameters. Such advantages are useful in precisely obtaining the atmospheric moments (Doppler frequency, spectrum width, etc.) from the synthesized beams required for wind estimation by the postset beam steering technique. In addition, the systematic improvements done in EV are much useful for complete wind profiling up to ~ 20 km with a temporal resolution of ~ 26 s, which is reported for the first time. Numéro de notice : A2013-367 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227334 En ligne : https://doi.org/10.1109/TGRS.2012.2227334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32505 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013071A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Derivation of tree skeletons and error assessment using LiDAR point cloud data of varying quality / Magnus Bremer in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)  

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Estimation de la qualité des résultats [d'une] classification sous ENVI / Nidal Aburajab (2013)

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Detecting negative spatial autocorrelation in georeferenced random variables / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 24 n°3-4 (march 2010)  

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Geospatial database organization and spatial decision analysis for biodiversity databases in web GIS environment / Harish Chandra Karnatak in Geocarto international, vol 25 n° 1 (February 2010)  

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Recent developments on direct relative orientation / H. Stewenius in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 4 (June - July 2006)  

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Numerical recipes / William H. Press (1988)

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