Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis

  • Mario I. Chacon-Murguía
  • Yearim Quezada-Holguín
  • Pablo Rivas-Perea
  • Sergio Cabrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

Dust storms are meteorological phenomena that may affect human life. Therefore, it is of great interest to work towards the development of a stand-alone dust storm detection system that may help to prevent and/or counteract its negative effects. This work proposes a dust storm detection system based on an Artificial Neural Network, ANN. The ANN is designed to identify not just dust storm areas but also vegetation and soil. The proposed ANN works on information obtained from multispectral images acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Before the multispectral information is fed to the ANN a process to remove cloud regions from images is performed in order to reduce the computational burden. A method to manage undefined and ambiguous ANN outputs is also proposed in the paper which significantly reduces the false positives rate. Results of this research present a suitable performance at detecting the dust storm events.

Keywords

Dust storm detection image segmentation neural network output analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mario I. Chacon-Murguía
    • 1
  • Yearim Quezada-Holguín
    • 1
  • Pablo Rivas-Perea
    • 2
  • Sergio Cabrera
    • 2
  1. 1.DSP & Vision LaboratoryChihuahua Institute of TechnologyChihuahuaMexico
  2. 2.ECEUniversity of Texas at El PasoUSA

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