The fusion of large scale classified side-scan sonar image mosaics

Scott Reed, Ioseba Tena Ruiz, Chris Capus, Yvan Petillot

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach. © 2006 IEEE.

Original languageEnglish
Pages (from-to)2049-2060
Number of pages12
JournalIEEE Transactions on Image Processing
Volume15
Issue number7
DOIs
Publication statusPublished - Jul 2006

Keywords

  • Classification
  • Fusion
  • Markov random fields
  • Mosaicing
  • Registration
  • Side-scan sonar (SSS)
  • Simultaneous localization and mapping (SLAM)

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