Statistical and neural pattern recognition methods for dust aerosol detection

Abstract

The dust storm phenomenon is a growing research area motivated by the potential harm that dust aerosols can cause to human beings, and by advances in remote-sensing technology. Nonetheless, formal studies on pattern recognition methods for dust aerosol detection are lacking and necessary. In this article, we present dust aerosol detection methods using statistical pattern recognition classifiers. Particularly, we investigate a support vector regression (SVR) approach and a large-scale approach to SVR. The feature set consists of multispectral thermal emissive bands from the Moderate Resolution Imaging Spectroradiometer (MODIS). We utilized four near-infrared bands: B20 (3.66–3.84 μm), B29 (8.40–8.70 μm), B31 (10.78–11.28 μm), and B32 (11.77–12.27 μm). Numerical performance evaluation shows that SVR outperforms other neural network-based classifiers in terms of a balanced error rate. Visually, both SVR paradigms accurately detect dust storms. The models demonstrated a strong ability to find non-trivial relationships within the spectral bands. The proposed detection methods are shown to be soil-independent and surface-invariant.

Acknowledgements

PRP performed part of this work while at NASA Goddard Space Flight Center under the supervision of Dr James C. Tilton. This work was supported in part by the National Council for Science and Technology (CONACyT), Mexico, under grant 193324/303732, and by the University of Texas at El Paso. The partial support of the Secretaría de Educación Pública - Dirección General de Relaciones Internacionales (SEP-DGRI) is also acknowledged.

Finally, the authors acknowledge the support of the Large-Scale Multispectral Multidimensional Analysis (LSMMA) Laboratory (http://www.lsmmalab.com), and also credit and acknowledge that all MODIS data were acquired via the Earth Observing System Data and Information System (EOSDIS), 2009. Earth Observing System Clearing House (ECHO)/Warehouse Inventory Search Tool (WIST) Version 10.X [online application]. Greenbelt, MD: EOSDIS, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA). URL: https://wist.echo.nasa.gov/api/

Notes

1. 1. The complete list of data granules referred to in Section 2 can be found in Rivas-Perea (2009). These events correspond to NASA Terra MODIS instrument. The reference will show level 1B file names.