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Multiple support vector machines for land cover change detection: an application for mapping urban extensions / H. Nemmour in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 2 (November 2006)
[article]
Titre : Multiple support vector machines for land cover change detection: an application for mapping urban extensions Type de document : Article/Communication Auteurs : H. Nemmour, Auteur ; Y. Chibani, Auteur Année de publication : 2006 Article en page(s) : pp 125 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alger
[Termes IGN] analyse comparative
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] occupation du sol
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] urbanisationRésumé : (Auteur) The reliability of support vector machines for classifying hyperspectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted. Copyright ISPRS Numéro de notice : A2006-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2006.09.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2006.09.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28254
in ISPRS Journal of photogrammetry and remote sensing > vol 61 n° 2 (November 2006) . - pp 125 - 133[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-06081 SL Revue Centre de documentation Revues en salle Disponible 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)
[article]
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)
[article]
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 Model-based prediction error uncertainty estimation for K-NN method / H.J. Kim in Remote sensing of environment, vol 104 n° 3 (15/10/2006)
[article]
Titre : Model-based prediction error uncertainty estimation for K-NN method Type de document : Article/Communication Auteurs : H.J. Kim, Auteur ; Erkki Tomppo, Auteur Année de publication : 2006 Article en page(s) : pp 257 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Betula (genre)
[Termes IGN] classification barycentrique
[Termes IGN] erreur moyenne quadratique
[Termes IGN] Finlande
[Termes IGN] image Landsat-TM
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Picea abies
[Termes IGN] Pinus (genre)
[Termes IGN] Populus (genre)
[Termes IGN] variogrammeRésumé : (Auteur) The k-nearest neighbour estimation method is one of the main tools used in multi-source forest inventories. It is a powerful non-parametric method for which estimates are easy to compute and relatively accurate. One downside of this method is that it lacks an uncertainty measure for predicted values and for areas of an arbitrary size. We present a method to estimate the prediction uncertainty based on the variogram model which derives the necessary formula for the k-nn method. A data application is illustrated for multi-source forest inventory data, and the results are compared at pixel level to the conventional RMSE method. We find that the variogram model-based method which is analytic, is competitive with the RMSE method. Numéro de notice : A2006-414 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.04.009 En ligne : https://doi.org/10.1016/j.rse.2006.04.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28138
in Remote sensing of environment > vol 104 n° 3 (15/10/2006) . - pp 257 - 263[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)
[article]
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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