Traditional and neural probabilistic multispectral image processing for the dust aerosol detection problem

This paper address the dust aerosol detection problem based on a probabilistic multispectral image analysis. Two classifiers are designed. First the Maximum Likelihood classifier is adapted to mode different types of atmospheric components. The second is a Probabilistic Neural Network (PNN) model. The data sets are MODIS multispectral bands from NASA Terra satellite. Findings indicate that the PNN presents a better classification performance than the ML classifier using manual segmentations as ground truth. The proposed algorithm is capable of real-time processing at 1 km resolutions which is an improvement compared to the 10 km resolution currently provided by other approaches.