Classifier fusion for multisensor image recognition

Fabio Roli; Giorgio Giacinto; Sebastiano Bruno Serpico

doi:10.1117/12.413886

19 January 2001 Classifier fusion for multisensor image recognition

Fabio Roli, Giorgio Giacinto, Sebastiano Bruno Serpico

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413886
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the “combination” of classifier outputs. Improvements in performances are related to the degree of “error diversity” among combined classifiers. Unfortunately, in remote-sensing image recognition applications, it may be difficult to design an ensemble that exhibit an high degree of error diversity. Recently, some researchers have pointed out the potentialities of “dynamic classifier selection” (DCS) as an alternative operation mechanism. DCS techniques are based on a function that selects the most appropriate classifier for each input pattern. The assumption of uncorrelated errors is not necessary for DCS because an “optimal” classifier selector always selects the most appropriate classifier for each test pattern. The potentialities of DCS have been motivated so far by experimental results on ensemble of classifiers trained using the same feature set. In this paper, we present an approach to multisensor remote-sensing image classification based on DCS. A selection function is presented aimed at choosing among classifiers created using different feature sets. The experimental results obtained in the classification of remote-sensing images and comparisons with different combination methods are reported.

Citation Download Citation

Fabio Roli, Giorgio Giacinto, and Sebastiano Bruno Serpico "Classifier fusion for multisensor image recognition", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413886

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