Supervised classification algorithms applied to the detection of atmospheric pollution

Abstract

The management of atmospheric polluting reject is based on the ability to measure this pollution. This paper deals with the case where no local sensor can be used, inducing the use of video to detect and evaluate the atmospheric pollution coming from large industrial facilities. This paper presents a comparison of different classifiers used in a monitoring system of polluting smokes detected by cameras. The data used in this work are stemming from a system of video analysis and signal processing. The database also includes the pollution level of plumes of smoke defined by an expert. Several Machine Learning techniques are tested and compared. The experimental results are obtained from a real world database of polluting rejects. The parameters of each type of classifier are split in three categories: learned parameters, parameters determined by a first step of the experimentation, and parameters set by the programmer. The comparison of the results of the best classifier of each type indicates that all of them provide good results. © 2009 Springer Berlin Heidelberg.