Analyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2
DOI :
https://doi.org/10.52638/rfpt.2018.310Mots-clés :
Classification, incertitude, évaluation de la précision thématique, GEOBIA, apprentissage actif, WorldView-2Résumé
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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Références
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Giacco, F., Thiel, C., Pugliese, L., Scarpetta, S., and Marinaro, M., 2010. Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs. IEEE Transactions on Geoscience and Remote Sensing 48, 3769—3779.
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Huth, J., Kuenzer, C., Wehrmann, T., Gebhardt, S., Tuan, V.Q., and Dech, S., 2012. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification. Remote Sensing 4, 2530—2553.
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Kullback, S., and Leibler, R.A., 1951. On Information and Sufficiency. Ann. Math. Statist. 22, 79—86.
Laben, C.A., and Brower, B.V., 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening.
Li, M., Ma, L., Blaschke, T., Cheng, L., and Tiede, D., 2016. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation 49, 87—98.
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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.
Löw, F., Michel, U., Dech, S., and Conrad, C., 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing 85, 102—119.
Löw, F., Knöfel, P., and Conrad, C., 2015. Analysis of uncertainty in multi-temporal object-based classification. ISPRS Journal of Photogrammetry and Remote Sensing 105, 91—106.
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.
Minasny, B., and McBratney, A.B., 2016. Digital soil mapping: A brief history and some lessons. Geoderma 264, Part B, 301—311.
Mulder, V.L., de Bruin, S., Schaepman, M.E., and Mayr, T.R., 2011. The use of remote sensing in soil and terrain mapping — A review. Geoderma 162, 1—19.
Nagendra, H., Lucas, R., Honrado, J.P., Jongman, R.H.G., Tarantino, C., Adamo, M., and Mairota, P., 2013. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators 33, 45—59.
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.
Pal, N.R., Bezdek. J.C., 1994. Measuring fuzzy uncertainty. IEEE Trans. Fuzzy Syst., 2, pp. 107—118
PostgreSQL Global Development Group, 2015. PostgreSQL 9.4.0 documentation, URL: http://www.opengeospatial.org/standards/, Open Geospatial Consortium.
Powers, R.P., Hermosilla, T., Coops, N.C., and Chen, G., 2015. Remote sensing and object-based techniques for mapping fine-scale industrial disturbances. International Journal of Applied Earth Observation and Geoinformation 34, 51—57.
Pu, R., and Landry, S., 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment 124, 516—533.
Quinlan, J.R., 1993. C4.5: Programs for Machine Learning (Morgan Kaufmann.
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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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