Boosting : a classification method for remote sensing | Boosting: une méthode de classification en télédétection
2007
Bailly, Jean-Stéphane | Arnaud, M. | Puech, C. | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS) | Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
[Departement_IRSTEA]DS [TR1_IRSTEA]METHODO / SYNERGIE
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显示更多 [+] 显示较少 [-]英语. This article sets out to demonstrate how boosting can serve as a supervised classification method, and to compare its results with those of conventional methods. The comparison begins with a theoretical example in which several criteria are varied: number of pixels per class, overlapping (or not) of radiometric values between classes, with and without spatial structuring of classes within the geographical space. The results are then compared with a real case study of land cover based on a multispectral SPOT image of the Sousson catchment area (South of France). It is seen that 1) maximum likelihood give better results than boosting when the radiometric values for each class are clearly separated. This advantage is lost as the number of pixels per class increases; 2) boosting is systematically better than maximum likelihood in the event of overlapping radiometric variable classes, whether or not there is a spatial structure.
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