Image-quality prediction of synthetic aperture sonar imagery

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Image-quality prediction of synthetic aperture sonar imagery
This work exploits several machine-learning techniques to address the problem of image-quality prediction of synthetic aperture sonar (SAS) imagery. The objective is to predict the correlation of sonar ping-returns as a function of range from the sonar by using measurements of sonar-platform motion and estimates of environmental characteristics. The environmental characteristics are estimated by effectively performing unsupervised seabed segmentation, which entails extracting wavelet-based features, performing spectral clustering, and learning a variational Bayesian Gaussian mixture model. The motion measurements and environmental features are then used to learn a Gaussian process regression model so that ping correlations can be predicted. To handle issues related to the large size of the data set considered, sparse methods and an out-of-sample extension for spectral clustering are also exploited. The approach is demonstrated on an enormous data set of real SAS images collected in th...
David P. Williams
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Authors David P. Williams
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