Descripteur
Termes IGN > mathématiques > statistique mathématique > régression
régressionSynonyme(s)analyse de régressionVoir aussi |
Documents disponibles dans cette catégorie (610)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Semisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
[article]
Titre : Semisupervised learning of hyperspectral data with unknown land-cover classes Type de document : Article/Communication Auteurs : G. Jun, Auteur ; J. Ghosh, Auteur Année de publication : 2013 Article en page(s) : pp 273 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] Botswana
[Termes IGN] classification bayesienne
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] régression
[Termes IGN] réponse spectrale
[Termes IGN] variationRésumé : (Auteur) Both supervised and semisupervised algorithms for hyperspectral data analysis typically assume that all unlabeled data belong to the same set of land-cover classes that is represented by labeled data. This is not true in general, however, since there may be new classes in the unexplored regions within an image or in areas that are geographically near but topographically distinct. This problem is more likely to occur when one attempts to build classifiers that cover wider areas; such classifiers also need to address spatial variations in acquired spectral signatures if they are to be accurate and robust. This paper presents a semisupervised spatially adaptive mixture model (SESSAMM) to identify land covers from hyperspectral images in the presence of previously unknown land-cover classes and spatial variation of spectral responses. SESSAMM uses a nonparametric Bayesian framework to apply spatially adaptive mechanisms to the mixture model with (potentially) infinitely many components. In this method, each component in the mixture has spatially adapted parameters estimated by Gaussian process regression, and spatial correlations between indicator variables are also considered. The proposed SESSAMM algorithm is applied to hyperspectral data from Botswana and from the DC Mall, where some classes are present only in the unlabeled data. SESSAMM successfully differentiates unlabeled instances of previously known classes from unknown classes and provides better results than the standard Dirichlet process mixture model and other alternatives. Numéro de notice : A2013-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198654 En ligne : https://doi.org/10.1109/TGRS.2012.2198654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32152
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 273 - 282[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013011A RAB Revue Centre de documentation En réserve L003 Disponible Understorey plant species show long‐range spatial patterns in forest patches according to distance‐to‐edge / Vincent Pellissier in Journal of vegetation science, vol 24 n° 1 (January 2013)
[article]
Titre : Understorey plant species show long‐range spatial patterns in forest patches according to distance‐to‐edge Type de document : Article/Communication Auteurs : Vincent Pellissier, Auteur ; Laurent Bergès, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 9 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] afforestation
[Termes IGN] distribution spatiale
[Termes IGN] forêt tempérée
[Termes IGN] France (administrative)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] régression logistiqueRésumé : (auteur) Questions : How does the presence of understorey plant species vary with distance‐to‐edge along very large periphery‐to‐interior and forest patch size gradients? Can forest core and periphery species profiles be identified? What life‐history traits can discriminate between forest core and forest periphery species?
Location : Temperate forests in the northern half of France.
Methods : Local climate, soil, stand characteristics and landscape metrics were collected on 19 989 plots in 1801 forest patches using data from the French National Forest Inventory. Very large distance‐to‐edge (3–1096 m) and patch size gradients (327–100 000 ha) were explored. Four logistic regression models were compared to determine the response patterns of 214 species to distance‐to‐edge, while controlling for patch size and local habitat quality (soil, climate and stand). The maximum distance of correlation between species occurrence and distance‐to‐edge was assessed using response curve characteristics. The relationships between life‐history traits (habitat preference, preference for ancient forests, reproduction mode, dispersal mode, life form and autecology) and species profile according to distance‐to‐edge were tested.
Results : Of the 214 species analysed, 40 had a core profile and 38 a periphery profile. The maximum distance of correlation was on average 748 m. Core species were more often species reproducing both by seed and vegetatively, ancient forest species, anemochores, bryophytes, pteridophytes, hemicryptophytes and acidophiles, whereas peripheral species were more often species reproducing by seed only, endozoochores, phanerophytes, thermophiles, basophiles, nitrogen‐demanding and heliophiles.
Conclusions : Significant periphery‐to‐core patterns of distribution were detected over much larger ranges than hitherto recognized for common understorey plant species. Plant traits differentiated forest core from forest periphery species. This deep gradient cannot be solely explained by the usual edge‐related biotic and abiotic factors. We hypothesized that it was due to edge displacement following general reforestation since ca. 1830. This edge shift created recent forests with new habitats on former agricultural lands where dispersal‐limited core species had slowly expanded and forest edge species regressed at variable speeds. This long periphery‐to‐interior gradient of presence has important implications for forest plant species distribution, dynamics and conservation.Numéro de notice : A2013-849 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/j.1654-1103.2012.01435.x Date de publication en ligne : 07/06/2012 En ligne : https://doi.org/10.1111/j.1654-1103.2012.01435.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97440
in Journal of vegetation science > vol 24 n° 1 (January 2013) . - pp 9 - 24[article]Cross-calibration of the total ozone unit (TOU) with the ozone monitoring instrument (OMI) and SBUV/2 for environmental applications / W. Wang in IEEE Transactions on geoscience and remote sensing, vol 50 n° 12 (December 2012)
[article]
Titre : Cross-calibration of the total ozone unit (TOU) with the ozone monitoring instrument (OMI) and SBUV/2 for environmental applications Type de document : Article/Communication Auteurs : W. Wang, Auteur ; L. Flynn, Auteur ; X. Zhang, Auteur ; Y. Wang, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4943 - 4955 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande ultraviolet
[Termes IGN] environnement
[Termes IGN] étalonnage d'instrument
[Termes IGN] Ozone Monitoring Instrument
[Termes IGN] régressionRésumé : (Auteur) A cross-sensor calibration technique is developed and applied to improve upon the prelaunch radiance calibration and characterization for the Total Ozone Unit (TOU) onboard the FengYun-3/A satellite. The Level 3 products from the National Aeronautics and Space Administration Ozone Monitoring Instrument (OMI) onboard the Earth Observing System Aura are used as input to a radiative transfer model to predict the TOU radiances and characterize the biases for the measurements over the Pacific Ocean in low- and midlatitudes. The coefficients are derived from a regression algorithm to adjust the TOU radiances. It is shown that, after the measurement bias correction, the biases between the retrieved total column ozone products from the TOU with those from the OMI Total Ozone Mapping Spectrometer (TOMS)-Version 8 products and those from a set of ground-based station measurements are 3 % and 5% , respectively. The variations in the estimated total ozone amounts from the TOU are consistent with those derived from Solar Backscatter Ultraviolet Radiometer instruments and OMI for a period from January 2010 to February 2011. Numéro de notice : A2012-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2210902 Date de publication en ligne : 17/09/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2210902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32093
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 12 (December 2012) . - pp 4943 - 4955[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2012121 RAB Revue Centre de documentation En réserve L003 Disponible The spatial prediction of tree species diversity in savanna woodlands of Southern Africa / G. Mutowo in Geocarto international, vol 27 n° 8 (December 2012)
[article]
Titre : The spatial prediction of tree species diversity in savanna woodlands of Southern Africa Type de document : Article/Communication Auteurs : G. Mutowo, Auteur ; Amon Murwira, Auteur Année de publication : 2012 Article en page(s) : pp 627 - 645 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre (flore)
[Termes IGN] biodiversité
[Termes IGN] image Ikonos
[Termes IGN] image Terra-ASTER
[Termes IGN] indice de végétation
[Termes IGN] prédiction
[Termes IGN] radiance
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] régression linéaire
[Termes IGN] savane
[Termes IGN] ZimbabweRésumé : (Auteur) In this study, we tested the utility of remotely sensed data in predicting tree species diversity in savanna woodlands. Specifically, we developed linear regression functions based on a combination of the coefficient of variation of near infrared (NIR) radiance and the soil-adjusted vegetation index (SAVI), both derived from advanced space-borne thermal emission and reflection radiometer satellite imagery. Using the regression functions in a Geographic Information System (GIS), we predicted the spatial variations in tree species diversity. Our results showed that tree species diversity can be predicted using a combination of the coefficient of variation of NIR radiance and SAVI. We conclude that remotely sensed data can be used to spatially predict tree species diversity in savanna woodlands. Numéro de notice : A2012-550 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.662530 Date de publication en ligne : 29/02/2012 En ligne : https://doi.org/10.1080/10106049.2012.662530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31996
in Geocarto international > vol 27 n° 8 (December 2012) . - pp 627 - 645[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2012081 RAB Revue Centre de documentation En réserve L003 Disponible Total variation spatial regularization for sparse hyperspectral unmixing / M. Iordache in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
[article]
Titre : Total variation spatial regularization for sparse hyperspectral unmixing Type de document : Article/Communication Auteurs : M. Iordache, Auteur ; J. Biuoucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2012 Article en page(s) : pp 4484 - 4502 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] information géographique
[Termes IGN] prise en compte du contexte
[Termes IGN] régressionRésumé : (Auteur) Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery. Numéro de notice : A2012-588 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2191590 Date de publication en ligne : 07/05/2012 En ligne : https://doi.org/ 10.1109/TGRS.2012.2191590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32034
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4484 - 4502[article]Active learning methods for biophysical parameter estimation / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 2 (October 2012)PermalinkA complete processing chain for shadow detection and reconstruction in VHR images / L. Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)PermalinkIntegration of remote sensing and GIS for evaluating soil erosion risk in northwestern Zhejiang, China / Jianqin Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)PermalinkThe affine constrained GNSS attitude model and its multivariate integer least-squares solution / Peter J.G. Teunissen in Journal of geodesy, vol 86 n° 7 (July 2012)PermalinkThe potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass / T.M. Basuki in Geocarto international, vol 27 n° 4 (July 2012)Permalink3-D mapping of a multi-layered Mediterranean forest using ALS data / António Ferraz in Remote sensing of environment, vol 121 (June 2012)PermalinkVisibility monitoring using conventional roadside cameras : Emerging applications / Raouf Babari in Transportation Research - Part C: Emerging Technologies, vol 22 (June 2012)PermalinkTracking human impact on current tree species distribution using plant communities / Daniel E. Silva in Journal of vegetation science, vol 23 n° 2 (April 2012)PermalinkDoes natural regeneration determine the limit of European beech distribution under climatic stress? / Daniel E. Silva in Forest ecology and management, vol 266 (15 February 2012)PermalinkCarbon Stock of European Beech Forest : A Case at M. Pizzalto, Italy / Aida Taghavi Bayat in APCBEE Procedia, vol 1 (2-20)Permalink