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Wind resource assessment from C-band SAR / M.B. Christiansen in Remote sensing of environment, vol 105 n° 1 (15/11/2006)
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Titre : Wind resource assessment from C-band SAR Type de document : Article/Communication Auteurs : M.B. Christiansen, Auteur ; W Koch, Auteur Année de publication : 2006 Article en page(s) : pp 68 - 81 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] direction
[Termes IGN] écart type
[Termes IGN] erreur systématique
[Termes IGN] gradient
[Termes IGN] image Envisat-ASAR
[Termes IGN] image ERS-SAR
[Termes IGN] Nord, mer du
[Termes IGN] vent
[Termes IGN] vitesseRésumé : (Auteur) Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. First, wind speeds retrieved from a series of 91 ERS-2 SAR and Envisat ASAR images, at moderate wind speeds (2–15 m s- 1), were validated against in situ measurements from an offshore mast in the North Sea. The wind direction input, necessary for SAR wind speed retrievals, was obtained from the meteorological mast and from a local gradient analysis of wind streaks in the SAR images. A wind speed standard deviation of not, vert, similar 1.1 m s- 1 was found when in situ wind directions were used. The use of local gradient wind directions yielded a standard deviation of not, vert, similar 1.3 m s- 1. Wind speeds retrieved from three geophysical model functions (CMOD-IFR2, CMOD4, and CMOD5) were compared. The best approximation to the in situ measurements of wind speed was found for CMOD-IFR2, despite a bias on the order of - 0.3 m s- 1. CMOD4 retrievals also underestimated the wind speed, whereas the bias on CMOD5 retrievals was negligible. Then, wind resource assessments were made from the SAR-based wind observations to show how errors in wind speed from the different SAR wind retrievals were reflected in the wind statistics. The mean wind speed, obtained for all of the 91 SAR scenes, was linked closely to the bias of SAR wind retrievals. Agreement to 1 15% of the in situ measurements was found for all the wind retrieval methods tested. The accuracy of power density estimates for the entire data set was evaluated by the standard deviation of SAR wind retrievals relative to the in situ measurements. SAR wind fields retrieved with CMOD-IFR2, using in situ wind direction inputs, exactly yielded the power density predicted from in situ measurements alone. The SAR-based wind resource assessment also corresponded well to predictions from longer time series of in situ measurements. This indicates that a reliable wind resource assessment may be achieved from a series of randomly selected SAR images. The findings presented here could be useful in future wind resource assessment based on SAR images. Copyright Elsevier Numéro de notice : A2006-503 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.06.005 En ligne : https://doi.org/10.1016/j.rse.2006.06.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28227
in Remote sensing of environment > vol 105 n° 1 (15/11/2006) . - pp 68 - 81[article]A novel method for mapping land cover changes: Incorporating time and space with geostatistics / A. Boucher in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)
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Titre : A novel method for mapping land cover changes: Incorporating time and space with geostatistics Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; K.C. Seto, Auteur ; A.G. Journel, Auteur Année de publication : 2006 Article en page(s) : pp 3427 - 3435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] données de terrain
[Termes IGN] filtre de déchatoiement
[Termes IGN] géostatistique
[Termes IGN] krigeage
[Termes IGN] série temporelle
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps. Copyright IEEE Numéro de notice : A2006-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.879113 En ligne : https://doi.org/10.1109/TGRS.2006.879113 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28252
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3427 - 3435[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)
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Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] reconnaissance automatiqueRésumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible Assessment of the processed SRTM-based elevation data by CGIAR using field from USA and Thailand and its relation to the terrain characteristics / Y. Gorokhovich in Remote sensing of environment, vol 104 n° 4 (30/10/2006)
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Titre : Assessment of the processed SRTM-based elevation data by CGIAR using field from USA and Thailand and its relation to the terrain characteristics Type de document : Article/Communication Auteurs : Y. Gorokhovich, Auteur ; A. Voustianiouk, Auteur Année de publication : 2006 Article en page(s) : pp 409 - 415 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de données
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] réalité de terrain
[Termes IGN] régression multiple
[Termes IGN] ThaïlandeRésumé : (Auteur) Shuttle radar topographic mission (SRTM) has created an unparalleled data set of global elevations that is freely available for modeling and environmental applications. The global availability (almost 80% of the Earth surface) of SRTM data provides baseline information for many types of the worldwide research. The processed SRTM 90 m digital elevation model (DEM) for the entire globe was compiled by Consultative Group for International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) and made available to the public via internet mapping interface. This product presents a great value for scientists dealing with terrain analysis, thanks to its easy download procedure and ready-to-use format. However, overall assessment of the accuracy of this product requires additional regional studies involving ground truth control and accuracy verification methods with higher level of precision, such as the global positioning system (GPS).
The study presented in this paper is based on two independent datasets collected with the same GPS system in Catskill Mountains (New York, USA) and Phuket (Thailand). Both datasets were corrected with differential base station data. Statistical analysis included estimation of absolute errors and multiple regression analysis with slope and aspect variables. Data were analyzed for each location separately and in combination. Differences in terrain and geographical location allowed independent interpretation of results.
The results of this study showed that absolute average vertical errors from CGIAR dataset can range from 7.58 1 0.60 m in Phuket to 4.07 1 0.47 m in Catskills (mean 1 S.E.M.). This is significantly better than a standard SRTM accuracy value indicated in its specification (i.e. 16 m). The error values have strong correlation with slope and certain aspect values. Taking into account slope and aspect considerably improved the accuracy of the CGIAR DEM product for terrain with slope values greater than 10°; however, for the terrain with slope values less than 10°, this improvement was found to be negligible. Copyright ElsevierNuméro de notice : A2006-495 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.05.012 En ligne : https://doi.org/10.1016/j.rse.2006.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28219
in Remote sensing of environment > vol 104 n° 4 (30/10/2006) . - pp 409 - 415[article]Comparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China / G. Yan in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)
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Titre : Comparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China Type de document : Article/Communication Auteurs : G. Yan, Auteur ; J.F. Mas, Auteur ; B.H. Maathuis, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 4039 - 4055 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte d'occupation du sol
[Termes IGN] charbon
[Termes IGN] classification orientée objet
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] image Terra-ASTER
[Termes IGN] incendie
[Termes IGN] précision de la classificationRésumé : (Auteur) Pixel-based and object-oriented classifications were tested for land-cover mapping in a coal fire area. In pixel-based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object-oriented classification, a region-growing multi-resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land-cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object-oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel-based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land-cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object-oriented image analysis approach gave a much higher accuracy than that obtained using the pixel-based approach. Copyright Taylor & Francis Numéro de notice : A2006-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600702632 En ligne : https://doi.org/10.1080/01431160600702632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28185
in International Journal of Remote Sensing IJRS > vol 27 n°18 - 19 - 20 (October 2006) . - pp 4039 - 4055[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06101 RAB Revue Centre de documentation En réserve L003 Disponible On comparing multifractal and classical features in minimum distance classification of AVHRR imagery / T. Parrinello in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)
PermalinkSatellite image classification using granular neural networks / D. Stathakis in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)
PermalinkGeodesic matching with free extremities / Laurent Garcin in Journal of Mathematical Imaging and Vision, vol 25 n° 3 (October 2006)
PermalinkReal-time monitoring and short-term forecasting of land surface phenology / M.A. White in Remote sensing of environment, vol 104 n° 1 (15/09/2006)
PermalinkTraining set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)
PermalinkImage restoration for resolution improvement of digital aerial images: a comparison of large format digital cameras / S. Becker in Revue Française de Photogrammétrie et de Télédétection, n° 183 (Septembre 2006)
PermalinkAerosol optical depth and land surface reflectance from multiangle AATSR measurements: global validation and intersensor comparisons / W.M.F. Grey in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
PermalinkComparison of space borne radar altimetry and airborne laser altimetry over sea ice in the Fram Strait / K.A. Giles in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)
PermalinkInter-comparison of NOAA-AVHRR and IRS-P4 (MSMR) derived sea surface temperatures / B. Jena in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)
PermalinkPerformance of change detection using remotely sensed data and evidential fusion: comparison of three cases of application / Sylvie Le Hégarat-Mascle in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)
PermalinkQuantifying spatial heterogeneity at the landscape scale using variogram models / S. Garrigues in Remote sensing of environment, vol 103 n° 1 (15 July 2006)
PermalinkIncorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach / A.E. Daniels in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)
PermalinkA new method to determine near surface air temperature from satellite observations / Ranjit Singh in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)
PermalinkScale sets image analysis / Laurent Guigues in International journal of computer vision, vol 68 n°3 (July 2006)
PermalinkA technique for generating natural colour images from false colour composite images / S.K. Patra in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)
PermalinkUrban land-use classification using variogram-based analysis with an aerial photograph / S.S. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 7 (July 2006)
PermalinkEnhancing a sequence of facial images combining multiple undersampled and compressed images / G. Scarmana in Photogrammetric record, vol 21 n° 114 (June - August 2006)
PermalinkHigh-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments / U. Narayan in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)
PermalinkPerformance metrics: how and when / S.A. Israel in Geocarto international, vol 21 n° 2 (June - August 2006)
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