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 the Baltic Sea.