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Proceedings Paper

A parameterized statistical sonar image texture model
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Paper Abstract

Single-point statistical properties of envelope-detected data such as signal returns from synthetic aperture radar and sonar have traditionally been modeled via the Rayleigh distribution and more recently by the K-distribution. Two-dimensional correlations that occur in textured non-Gaussian imagery are more difficult to model and estimate than Gaussian textures due to the nonlinear transformations of the time series data that occur during envelope detection. In this research, textured sonar imagery is modeled by a correlated K-distribution. The correlated K-distribution is explained via the compound representation of the one-dimensional K-distribution probability density function. After demonstrating the model utility using synthetically generated imagery, model parameters are estimated from a set of textured sonar images using a nonlinear least-squares fit algorithm. Results are discussed with regard to texture segmentation applications.

Paper Details

Date Published: 29 April 2008
PDF: 12 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69530K (29 April 2008); doi: 10.1117/12.777185
Show Author Affiliations
J. Tory Cobb, Naval Surface Warfare Ctr. (United States)
K. Clint Slatton, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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