Analyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2
DOI:
https://doi.org/10.52638/rfpt.2018.310Keywords:
Classification, incertitude, évaluation de la précision thématique, GEOBIA, apprentissage actif, WorldView-2Abstract
L'évaluation de la précision des cartes thématiques produites par télédétection est une finalité de tout processus de classification modélisant le paysage. Reposant traditionnellement sur la matrice de confusion, elle peut être complétée par des méthodes alternatives plus à même de prendre en compte le biais relatif à la sélection des échantillons d'apprentissage, ainsi que par l'emploi d'approches représentant spatialement l'incertitude inhérente aux classifications. Une telle démarche est adoptée dans cet article, en évaluant la précision à l'aide des estimateurs du Maximum de Probabilité a Posteriori, puis en déterminant, pour chaque unité de carte, des mesures d'incertitude : l'entropie quadratique, la divergence de Kullback-Leibler et un indice de concordance qualitatif. Ces traitements sont analysés et comparés selon 3 classifieurs, Random Forest, C5.0 et l'Analyse Discriminante Linéaire et selon 4 stratégies de classification : classifieurs seuls, classifieurs avec procédure de bagging, classifieurs avec procédure d'apprentissage actif et classifieurs avec procédure d'apprentissage actif et de bagging. Les résultats obtenus soulignent la complémentarité des estimateurs de précision pour mettre en évidence un biais dans l'évaluation de la précision ou dans la détermination des probabilités a posteriori, et justifie la prise en considération des indices d'incertitude comme source d'informations sur la distribution spatiale des erreurs de cartographie.
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References
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Laben, C.A., and Brower, B.V., 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening.
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Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Van Coillie, F.M.B., De Baets, B., and Verhoest, N.E.C., 2012. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification. International Journal of Applied Earth Observation and Geoinformation 19, 173—184.
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Mellor, A., Boukir, S., Haywood, A., and Jones, S., 2015. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS Journal of Photogrammetry and Remote Sensing 105, 155—168.
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Niculescu-Mizil, A., and Caruana, R., 2005. Predicting good probabilities with supervised learning. In Proceedings of the 22nd International Conference on Machine Learning, (ACM), pp. 625—632.
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2018-04-19
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Messner, F., Corbonnois, J., & Tchitouo Ntenzou, F. S. (2018). Analyse de l’incertitude et de la précision thématique de classifications GEOBIA d’une image WorldView-2. Revue Française de Photogrammétrie et de Télédétection, (216), 19–37. https://doi.org/10.52638/rfpt.2018.310
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