Unsupervised domain transfer of latent Dirichlet allocation derived representations from synthetic aperture sonar imagery

Jason C. Isaacs

doi:10.1117/12.2016395

7 June 2013 Unsupervised domain transfer of latent Dirichlet allocation derived representations from synthetic aperture sonar imagery

Jason C. Isaacs

Author Affiliations +

Proceedings Volume 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII; 87090G (2013) https://doi.org/10.1117/12.2016395
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Identifying the important discriminating information demonstrated by objects in SAS imagery is important for automatic target recognition. We present a method for determining which information is important using a generative model for documents, introduced by Blei, Ng, and Jordan³ in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We use this algorithm to analyze synthetic aperture sonar data by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the SAS data, consistent with the class designations provided, and demonstrate the transfer of this knowledge across sensor domains.

Citation Download Citation

Jason C. Isaacs "Unsupervised domain transfer of latent Dirichlet allocation derived representations from synthetic aperture sonar imagery", Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87090G (7 June 2013); https://doi.org/10.1117/12.2016395

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