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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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible 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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-06101 RAB Revue Centre de documentation En réserve L003 Disponible Fuzzy classification: a case study using Landsat TM images in Iran / A.M. Lak in GIM international, vol 20 n° 7 (July 2006)
[article]
Titre : Fuzzy classification: a case study using Landsat TM images in Iran Type de document : Article/Communication Auteurs : A.M. Lak, Auteur ; M. Hamrah, Auteur ; G.H. Majdabadi, Auteur Année de publication : 2006 Article en page(s) : pp 42 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification barycentrique
[Termes IGN] classification floue
[Termes IGN] classification hybride
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] distribution de Gauss
[Termes IGN] image Landsat-TM
[Termes IGN] Iran
[Termes IGN] MatlabRésumé : (Editeur) Extraction of information from satellite images is a solution for countries without up-to-date base maps. Such images can be easily obtained and cover vast areas. Information is mostly extracted using multispectral classification, but many methods have been developed. The authors examined 'fuzzy classification' and found it more accurate and requiring less computing time than other methods. Copyright GITC Numéro de notice : A2006-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27980
in GIM international > vol 20 n° 7 (July 2006) . - pp 42 - 43[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 061-06071 RAB Revue Centre de documentation En réserve L003 Disponible Incorporating 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)
[article]
Titre : Incorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach Type de document : Article/Communication Auteurs : A.E. Daniels, Auteur Année de publication : 2006 Article en page(s) : pp 2949 - 2975 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte d'occupation du sol
[Termes IGN] classe d'objets
[Termes IGN] classification à base de connaissances
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données auxiliaires
[Termes IGN] feuillu
[Termes IGN] forêt tropicale
[Termes IGN] interprétation automatique
[Termes IGN] occupation du sol
[Termes IGN] précision de la classificationRésumé : (Auteur) For some tropical regions, remote sensing of land cover yields unacceptable results, particularly as the number of land cover classes increases. This research explores the utility of incorporating domain knowledge and multiple algorithms into land cover classifications via a rule-based algorithm for a series of satellite images. The proposed technique integrates the fundamental, knowledge-based interpretation elements of remote sensing without sacrificing the ease and consistency of automated, algorithm-based processing. Compared with results from a traditional maximum likelihood algorithm, classification accuracy was improved substantially for each of the six land cover classes and all three years in the image series. Use of domain knowledge proved effective in accurately classifying problematic tropical land covers, such as tropical deciduous forest and seasonal wetlands. Results also suggest that ancillary data may be most useful in the classification of historic images, where the greatest improvement was observed relative to results from maximum likelihood. The cost of incorporating contextual knowledge and extensive spatial data sets may be justified, since results from the proposed technique suggest a considerable improvement in accuracy may be achieved. Copyright Taylor & Francis Numéro de notice : A2006-310 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600567753 En ligne : https://doi.org/10.1080/01431160600567753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28034
in International Journal of Remote Sensing IJRS > vol 27 n°12-13-14 (July 2006) . - pp 2949 - 2975[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-06071 RAB Revue Centre de documentation En réserve L003 Disponible Some issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)
[article]
Titre : Some issues in the classification of DAIS hyperspectral data Type de document : Article/Communication Auteurs : M. Pal, Auteur ; Paul M. Mather, Auteur Année de publication : 2006 Article en page(s) : pp 2895 - 2916 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classificateur paramétrique
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Espagne
[Termes IGN] image DAIS
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] qualité du processus
[Termes IGN] transformation orthogonaleRésumé : (Auteur) Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision-tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. Copyright Taylor & Francis Numéro de notice : A2006-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500185227 En ligne : https://doi.org/10.1080/01431160500185227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28033
in International Journal of Remote Sensing IJRS > vol 27 n°12-13-14 (July 2006) . - pp 2895 - 2916[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-06071 RAB Revue Centre de documentation En réserve L003 Disponible Apport de la classification combinée supervisée et non supervisée d'une image Landsat ETM+ à la cartographie géologique de la boutonnière de Kerdous, anti-atlas, Maroc / M. Hakdaoui in Photo interprétation, vol 42 n° 2 (Juin 2006)PermalinkAutomatic building detection using the Dempster-Shafer algorithm / Y.H. Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 4 (April 2006)PermalinkCaractérisation d'un habitat forestier tempéré par télédétection satellitale pour le suivi de populations aviennes : cas des mésanges en forêt de Larivour (Aube, France) / V. Godard in Photo interprétation, vol 41 n° 4 (Novembre 2005)PermalinkOn the relationship between training sample size data dimensionality: Monte Carlo analysis of broadland multi-temporal classification / T.G. Van Niel in Remote sensing of environment, vol 98 n° 4 (30/10/2005)PermalinkTypologie des paysages forestiers du sud du massif de Fontainebleau après la tempête de décembre 1999 / V. Godard in Revue internationale de géomatique, vol 15 n° 3 (septembre – novembre 2005)PermalinkCloud-free satellite image mosaics with regression trees and histogram matching / E.H. Helmert in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 9 (September 2005)PermalinkUtilisation des images satellitaires Spot pour la cartographie des types de peuplements de la forêt de la Mamora (Maroc) / Abderrahman Aafi in Revue Française de Photogrammétrie et de Télédétection, n° 178 (Septembre 2005)PermalinkEstimating and accommodating uncertainty through the soft classification of remote sensing data / M.A. Ibrahim in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)PermalinkRadial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)PermalinkSatellite remote sensing for detailed landslide inventories using change detection and image fusion / J. Nichol in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)Permalink