Multiple classifier systems in remote sensing: From basics to recent developments

Abstract. In this paper, we present some recent developments of Mul-tiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and ran-dom forests) are briefly described and applied to multisource data (satel-lite multispectral images, elevation, slope and aspect data) for landcover classification. In a second part, special attention is given to Support Vec-tor Machines (SVM) based algorithms. In particular, the fusion of two classifiers using the spectral and the spatial information, respectively, is discussed in the frame of hyperspectral remote sensing for the classifica-tion of urban areas. In all the cases, MCS provide a significant improve-ment of the classification accuracies. In order to address new challenges for the analysis of remote sensing data, MCS provide invaluable tools to handle situations with an ever growing complexity. Examples include extraction of multiple features from one data set, use of multi-sensor data, and complementary use of several algorithms in a decision fusion scheme.