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Proceedings Paper

Multiresolution and directional filtering techniques for detecting dust storm direction in satellite imagery
Author(s): Mohammed Q. Alkhatib; Sergio D. Cabrera
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Paper Abstract

This paper presents a new method for finding the direction of a dust storm in satellite images including the 5-band NOAA-AVHRR imagery that were used in our previous work. The previous methods for obtaining the prominent direction of the dust storms involved the combination of edge detectors and local spectral-domain classification techniques applied to subimages/blocks. These approaches produced promising results but have the limitation of not providing consistent results among the subimages that overlap the dust storm region. In this paper, other algorithms like wavelets and state-of-the-art directional filters, based on the contourlet transform, are used to help us determine the direction with more precision and consistency among the relevant subimages. Before applying the directional filtering to the candidate region of the multispectral image, a preprocessing step involves passing the image through a nonsubsampled pyramid selective amplification, this preprocessing step is required in order to enhance the image and improve its directional streaks, in turn, this will help improve the performance of the directional filter to get better and more consistent results. For AVHRR images, our methodology involves applying directional filtering on bands 4 or 5 since these wavelengths highlight the absorption and subsequent emission of thermal radiation by the silicate particles in the dust storms. Directional filtering is applied on these image bands at different angles where energy measurements are computed to find the prominent direction of the dust storm. The presence of a prominent direction in the texture of the candidate region of the dust storm can be used as a verification of its presence in an automated detection system.

Paper Details

Date Published: 20 May 2011
PDF: 11 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480V (20 May 2011); doi: 10.1117/12.887076
Show Author Affiliations
Mohammed Q. Alkhatib, The Univ. of Texas at El Paso (United States)
Sergio D. Cabrera, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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