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Abstract:

In recent years, the remote-sensing community has became very interested in applying neural networks to image classification and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a "panacea". The superiority of one algorithm over the other strongly depends on the selected data set and on the efforts devoted to the "designing phases" of algorithms. In this paper, we propose the use of "ensembles " of neural and statistical classification algorithms as an alternative approach based on the exploitation of the complementary characteristics of different classifiers. Classification results provided by image classifiers contained in these ensembles are "merged " according to statistical combination methods. Experimental results on a multisensor remotesensing data set point out that the use of classifiers ensembles can constitute a valid alternative to the development of new classification algorithms "more complex " than the present ones. In particular, we show that the combination of results provided by statistical and neural algorithms provides classification accuracies better than the ones obtained by single classifiers after long "designing phases".

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