Youanta, M., Kouamé, K.F., Koudou, A., Adja, M.G., Baka, D., Lasm, T., De Lasme, O., Jourda, J.P. and Biémi, J. (2014) Apport de la Cartographie Lithostructurale par Imagerie Satellitaire Landsat 7 à la Connaissance des Aquifères du Socle Précambrien de la Région de Bondoukou (Nord-Est de la Côte D’ivoire). [Contribution of Lithostructural Mapping by Landsat 7 Satellite Imagery to the Knowledge of the Aquifers of the Precambrian Base of the Bondoukou Region (Northeast of Côte d’Ivoire).] International Journal of Innovation and Applied Studies, 7, 892-910.

has been cited by the following article:

• TITLE: Modeling and Characterization of Vegetation, Aquatic and Mineral Surfaces Using the Theory of Plausible and Paradoxical Reasoning from Satellite Images: Case of the Toumodi-Yamoussoukro-Tiébissou Zone in V Baoulé (Côte d’Ivoire)

  AUTHORS: Jean-Claude Okaingni, Sié Ouattara, Adles Kouassi, Wognin J. Vangah, Aubin K. Koffi, Alain Clement

  KEYWORDS: Theory of Plausible and Paradoxical Reasoning, PCR5 Rule, ASTER Satellite Images, NDVI, MNDWI, NDBaI, Modeling, Classification

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.7 No.10, October 26, 2017

  ABSTRACT: In this paper, the theory of plausible and paradoxical reasoning of Dezert- Smarandache (DSmT) is used to take into account the paradoxical charac-ter through the intersections of vegetation, aquatic and mineral surfaces. In order to do this, we developed a classification model of pixels by aggregating information using the DSmT theory based on the PCR5 rule using the ∩NDVI, ∩MNDWI and ∩NDBaI spectral indices obtained from the ASTER satellite images. On the qualitative level, the model produced three simple classes for certain knowledge (E, V, M) and eight composite classes including two union classes characterizing partial ignorance ({E,V}, {M,V}) and six classes of intersection of which three classes of simple intersection (E∩V, M∩V, E∩M) and three classes of composite intersection (E∩{M,V}, M∩{E,V}, V∩{E,M}), which represent paradoxes. This model was validated with an average rate of 93.34% for the well-classified pixels and a compliance rate of the entities in the field of 96.37%. Thus, the model 1 retained provides 84.98% for the simple classes against 15.02% for the composite classes.