Paper
27 April 2009 Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery
S. Janugani, V. Jayaram, S. D. Cabrera, J. G. Rosiles, T. E. Gill, N. Rivera Rivera
Author Affiliations +
Abstract
In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Janugani, V. Jayaram, S. D. Cabrera, J. G. Rosiles, T. E. Gill, and N. Rivera Rivera "Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341G (27 April 2009); https://doi.org/10.1117/12.820235
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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Image filtering

Visualization

Binary data

Image processing

Mathematics

Linear filtering

Image enhancement

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