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statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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Accuracy of forest mapping based on Landsat TM data and a kNN-based method / K. Gjertsen in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
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[article]
Titre : Accuracy of forest mapping based on Landsat TM data and a kNN-based method Type de document : Article/Communication Auteurs : K. Gjertsen, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 420 - 430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] données multisources
[Termes IGN] forêt
[Termes IGN] image Landsat-TM
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Norvège
[Termes IGN] précision de la classificationRésumé : (Auteur) A multi-source forest inventory (MSFI) method has been developed for use in the Norwegian National Forest Inventory (NFI). The method is based on a k-nearest neighbour rule and uses field plots from the NFI, land cover maps, and satellite image data from Landsat Thematic Mapper. The inventory method is used to produce maps of selected forest variables and to estimate the selected forest variables for large areas such as municipalities. In this study, focus has been on the qualitative variables ‘dominating species group’ and ‘development class’ because these variables are of central interest to forest managers. A mid-summer Landsat 5 TM scene was used as image data, and all NFI plots inside the scene were used as a reference dataset. The relationship between the spectral bands and the forest variables was analysed, and it was found that the levels of association were low. A leave-one-out method based on the reference dataset was used to estimate the pixel-level accuracies. They were found to be relatively low with 63% agreement for species groups. An independent control survey was available for a municipality and estimates from the MSFI were compared to it. The levels of error were quite high. It was concluded that the large area estimates were biased by the reference dataset. Copyright Elsevier Numéro de notice : A2007-410 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.018 En ligne : https://doi.org/10.1016/j.rse.2006.08.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28773
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 420 - 430[article]Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
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[article]
Titre : Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium Type de document : Article/Communication Auteurs : F.M.B. Van Coillie, Auteur ; L.P.C. Verbeke, Auteur ; R.R. DE Wulf, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 476 - 487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] Flandre (Belgique)
[Termes IGN] forêt tempérée
[Termes IGN] image Ikonos
[Termes IGN] segmentation d'imageRésumé : (Auteur) Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p Numéro de notice : A2007-412 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.03.020 En ligne : https://doi.org/10.1016/j.rse.2007.03.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28775
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 476 - 487[article]vol 110 n° 4 - 30/10/2007 - Forestsat 2007 (Bulletin de Remote sensing of environment) / Ronald E. McRoberts
[n° ou bulletin]
Titre : vol 110 n° 4 - 30/10/2007 - Forestsat 2007 Type de document : Périodique Auteurs : Ronald E. McRoberts, Éditeur scientifique ; D.N.M. Donhogue, Éditeur scientifique Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] caméra numérique
[Termes IGN] cartographie automatique
[Termes IGN] chambre métrique
[Termes IGN] classification barycentrique
[Termes IGN] forêt
[Termes IGN] image Landsat-ETM+
[Termes IGN] image SPOT 5
[Termes IGN] inventaire forestier (techniques et méthodes)Numéro de notice : 110-0719 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=10440 [n° ou bulletin] Contient
- Accuracy of forest mapping based on Landsat TM data and a kNN-based method / K. Gjertsen in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
- The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes / T. Koukal in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
- Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes / T. Koukal in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
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[article]
Titre : The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes Type de document : Article/Communication Auteurs : T. Koukal, Auteur ; F. Suppan, Auteur ; W. Schneider, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 431 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Autriche
[Termes IGN] classification barycentrique
[Termes IGN] étalonnage radiométrique
[Termes IGN] forêt
[Termes IGN] image Landsat-TM
[Termes IGN] point d'appui
[Termes IGN] régression linéaireRésumé : (Auteur) The k-nearest-neighbour (kNN) algorithm is widely applied for the estimation of forest attributes using remote sensing data. It requires a large amount of reference data to achieve satisfactory results. Usually, the number of available reference plots for the kNN-prediction is limited by the size of the area covered by a terrestrial reference inventory and remotely sensed imagery collected from one overflight. The applicability of kNN could be enhanced if adjacent images of different acquisition dates could be used in the same estimation procedure. Relative radiometric calibration is a prerequisite for this. This study focuses on two empirical calibration methods. They are tested on adjacent LANDSAT TM scenes in Austria. The first, quite conventional one is based on radiometric control points in the overlap area of two images and on the determination of transformation parameters by linear regression. The other, recently developed method exploits the kNN-cross-validation procedure. Performance and applicability of both methods as well as the impact of phenology are discussed. Copyright Elsevier Numéro de notice : A2007-411 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.016 En ligne : https://doi.org/10.1016/j.rse.2006.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28774
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 431 - 437[article]An elevation of void-filling interpolation methods for SRTM data / H.I. Reuter in International journal of geographical information science IJGIS, vol 21 n° 9-10 (october 2007)
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[article]
Titre : An elevation of void-filling interpolation methods for SRTM data Type de document : Article/Communication Auteurs : H.I. Reuter, Auteur ; A. Nelson, Auteur ; A. Jarvis, Auteur Année de publication : 2007 Article en page(s) : pp 983 - 1008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] fonction spline d'interpolation
[Termes IGN] image SRTM
[Termes IGN] interpolation
[Termes IGN] interpolation linéaire
[Termes IGN] interpolation spatiale
[Termes IGN] krigeage
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] Triangulated Irregular NetworkRésumé : (Auteur) The Digital Elevation Model that has been derived from the February 2000 Shuttle Radar Topography Mission (SRTM) has been one of the most important publicly available new spatial data sets in recent years. However, the 'finished' grade version of the data (also referred to as Version 2) still contains data voids (some 836,000 km2) - and other anomalies - that prevent immediate use in many applications. These voids can be filled using a range of interpolation algorithms in conjunction with other sources of elevation data, but there is little guidance on the most appropriate void-filling method. This paper describes: (i) a method to fill voids using a variety of interpolators, (ii) a method to determine the most appropriate void-filling algorithms using a classification of the voids based on their size and a typology of their surrounding terrain; and (iii) the classification of the most appropriate algorithm for each of the 3,339,913 voids in the SRTM data. Based on a sample of 1304 artificial but realistic voids across six terrain types and eight void size classes, we found that the choice of void-filling algorithm is dependent on both the size and terrain type of the void. Contrary to some previous findings, the best methods can be generalised as: kriging or inverse distance weighting interpolation for small and medium size voids in relatively flat low-lying areas; spline interpolation for small and medium-sized voids in high-altitude and dissected terrain; triangular irregular network or inverse distance weighting interpolation for large voids in very flat areas, and an advanced spline method (ANUDEM) for large voids in other terrains. Copyright Taylor & Francis Numéro de notice : A2007-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810601169899 En ligne : https://doi.org/10.1080/13658810601169899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28916
in International journal of geographical information science IJGIS > vol 21 n° 9-10 (october 2007) . - pp 983 - 1008[article]Réservation
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PermalinkExtraction of spectral channels from hyperspectral images for classification purposes / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)
PermalinkFFT-enhanced IHS transform method for fusing high-resolution satellite images / Y. Ling in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 6 (February 2007)
PermalinkRegional products for the Baltic Sea using MERIS data / H. Krawczyk in International Journal of Remote Sensing IJRS, vol 28 n°3-4 (February 2007)
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