Abstract
This paper address the detection of dust storms based on a probabilistic analysis of multispectral images. We develop a feature set based on the analysis of spectral bands reported in the literature. These studies have focused on the visual identification of the image channels that reflect the presence of dust storms through correlation with meteorological reports. Using this feature set we develop a Maximum Likelihood classifier and a Probabilistic Neural Network (PNN) to automate the dust storm detection process. The data sets are MODIS multispectral bands from NASA Terra satellite. Findings indicate that the PNN provides improved classification performance with reference to the ML classifier. Furthermore, the proposed schemes allow real-time processing of satellite data at 1 km resolutions which is an improvement compared to the 10 km resolution currently provided by other detection methods.
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Rivas-Perea, P., Rosiles, J.G., Murguia, M.I.C., Tilton, J.J. (2010). Automatic Dust Storm Detection Based on Supervised Classification of Multispectral Data. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_27
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DOI: https://doi.org/10.1007/978-3-642-15111-8_27
Publisher Name: Springer, Berlin, Heidelberg
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