NDLI: Image-quality prediction of synthetic aperture sonar imagery

Content Provider	IEEE Xplore Digital Library
Author	Williams, D.P.
Copyright Year	2010
Description	Author affiliation: NATO Undersea Research Centre, Viale San Bartolomeo 400, 19126 La Spezia (SP), Italy (Williams, D.P.)
Abstract	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 the Baltic Sea.
Starting Page	2114
Ending Page	2117
File Size	213758
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Synthetic aperture sonar Sonar measurements Motion measurement Sea measurements Motion estimation Image segmentation Data mining Feature extraction Bayesian methods Gaussian processes Large Data Sets Image-Quality Prediction Gaussian Process Regression Spectral Clustering Variational Bayesian Gaussian Mixture Models
Content Type	Text
Resource Type	Article

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