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Sampling approaches for one-pass land-use/land-cover change mapping / Zhi Huang in International Journal of Remote Sensing IJRS, vol 31 n° 6 (March 2010)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 31 n° 6 (March 2010) . - pp 1543 - 1554 Titre : Sampling approaches for one-pass land-use/land-cover change mapping Type de document : Article/Communication Auteurs : Zhi Huang, Auteur ; Xiuping Jia, Auteur ; Linlin Ge, Auteur Année de publication : 2010 Article en page(s) : pp 1543 - 1554 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'occupation du sol [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] échantillonnage d'image [Termes IGN] occupation du sol [Termes IGN] superposition d'images [Termes IGN] traitement d'image [Termes IGN] utilisation du sol Résumé : (Auteur) In land-use/land-cover change (LULCC) mapping, training fields for changed classes are often difficult to identify. In this study, a new sampling strategy is proposed for mapping LULCCs, in which change and no-change samples are obtained directly from overlaid bi-temporal images with a small shift or rotation. In this way, the artificial changes created maintain a certain degree of geographic information. This method is compared with a simulated sampling approach in which training samples of each land-use/land-cover type are selected separately from individual images and then cross-combined to form the 'from-to' classes. The case study demonstrates that both sampling strategies ease the difficulties in performing one-pass classification for LULCC detection and yield more accurate LULCC maps than that of the traditional two-step post-classification comparison method. Between the two sampling strategies, the proposed one provides higher testing accuracy than the simulated sampling approach, while the latter is easier to implement. Numéro de notice : A2010-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160903475399 En ligne : https://doi.org/10.1080/01431160903475399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30449 [article]

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Sensivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model / Zhi Huang in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n°11-12 (november 2009) . - pp 1433 - 1452 Titre : Sensivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model Type de document : Article/Communication Auteurs : Zhi Huang, Auteur ; S.W. Laffan, Auteur Année de publication : 2009 Article en page(s) : pp 1433 - 1452 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de sensibilité [Termes IGN] carte de la végétation [Termes IGN] classification par arbre de décision [Termes IGN] erreur de classification [Termes IGN] erreur de positionnement [Termes IGN] image Landsat-TM [Termes IGN] incertitude des données [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de terrain Résumé : (Auteur) We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one-at-a-time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non-linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as +2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs. Copyright Taylor & Francis Numéro de notice : A2009-515 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/13658810802634949 En ligne : https://doi.org/10.1080/13658810802634949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30144 [article]

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079-09071	RAB	Revue	Centre de documentation	En réserve L003	Disponible
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Representing and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 603 - 621 Titre : Representing and reducing error in natural-resource classification using model combination Type de document : Article/Communication Auteurs : Zhi Huang, Auteur Année de publication : 2005 Article en page(s) : pp 603 - 621 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par arbre de décision [Termes IGN] classification par réseau neuronal [Termes IGN] erreur d'attribut [Termes IGN] erreur d'échantillon [Termes IGN] précision de la classification [Termes IGN] propagation d'erreur [Termes IGN] ressources naturelles Résumé : (Auteur) Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster-Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster-Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a 'majority voting system', a simple average, Dempster-Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction. Numéro de notice : A2005-239 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810500032446 En ligne : https://doi.org/10.1080/13658810500032446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27376 [article]

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079-05051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible