Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 371-375, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-371-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
24 Sep 2013
A COMPARISON OF EMPIRICAL AND INTELIGENT METHODS FOR DUST DETECTION USING MODIS SATELLITE DATA
M. Shahrisvand and M. Akhoondzadeh Remote Sensing Division, Department of Surveying and Geomatic Engineering, College of Engineering, University of Tehran, Iran
Keywords: Classification, Dust detection, Decision tree, ANN, SVM, MODIS Abstract. Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (Aerosol Optical Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to assess the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (Aerosol Optical Thickness) product of NASA.
Conference paper (PDF, 722 KB)


Citation: Shahrisvand, M. and Akhoondzadeh, M.: A COMPARISON OF EMPIRICAL AND INTELIGENT METHODS FOR DUST DETECTION USING MODIS SATELLITE DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 371-375, https://doi.org/10.5194/isprsarchives-XL-1-W3-371-2013, 2013.

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