Semi-Automatic Identification of Submarine Pipelines with Synthetic Aperture Sonar Images

References

• Aridgides, T., M. Fernandez, and G. Dobeck. 2001. Fusion of adaptive algorithms for the classification of sea mines using high resolution side scan sonar in very shallow water. In: MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No. 01CH37295), 135–142. IEEE.
   Google Scholar
• Ayres Neto, A.,. G. P. Rodrigues, and I. D. Alvarenga. 2017. Seabed mapping with HISAS sonar for decommissioning projects. Sea Technology 58:15–8.
   Web of Science ®Google Scholar
• Buemi, M. E., A. C. Frery, and H. S. Ramos. 2014. Speckle reduction with adaptive stack filters. Pattern Recognition Letters 36:281–7.
   Web of Science ®Google Scholar
• Chaillan, F., C. Fraschini, and P. Courmontagne. 2007. Speckle noise reduction in SAS imagery. Signal Processing 87 (4):762–81.
   Web of Science ®Google Scholar
• Cho, H., and S.-C. Yu. 2015. Real-time sonar image enhancement for AUV-based acoustic vision. OceanEngineering 104:568–79.
   Web of Science ®Google Scholar
• ENVI. 2015. ENVI 5.0.Broomfield, United States: Exelis information Solution, Inc., a subsisidiary of Harris Corporation.
   Google Scholar
• Facon, J. 1996. Morfologia Matemática: Teorias e Aplicações, 320. Curitiba: Editora Universitária Champagnat da Pontifícia Universidade Católica do Paraná.
   Google Scholar
• Fernandes, V. H., D. D. Rodrigues, A. Ayres Neto, and L. G. Barbosa. 2019. Modeling positional uncertainty for hydrographic surveys with AUV. Journal of Surveying Engineering 145 (1):04018014.
   Web of Science ®Google Scholar
• Fernandes, V. H., N. G. Medeiros, D. D. Rodrigues, A. A. Neto, and J. C. Oliveira. 2019. Redução de ruídos speckle no mapeamento de estruturas lineares submarinas em imagens de sonar de abertura sintética. Revista Brasileira de Cartografia 71 (1):54–74.
   Google Scholar
• Gade, M., W. Wang, and L. Kemme. 2018. On the imaging of exposed intertidal flats by single-and dual-co-polarization synthetic aperture radar. Remote Sensing of Environment 205:315–28.
   Web of Science ®Google Scholar
• Hansen, R. E. 2019. Mapping the ocean floor in extreme resolution using interferometric synthetic aperture sonar. In: Proceedings of Meetings on Acoustics ICU, 055003. Acoustical Society of America.
   Google Scholar
• Hayes, M. P., and P. T. Gough. 2009. Synthetic aperture sonar: A review of current status. IEEE Journal of Oceanic Engineering 34 (3):207–24.
   Web of Science ®Google Scholar
• Hoggarth, A., and K. Kenny. 2014. Using synthetic aperture sonar as an effective hydrographic survey tool. In: Oceans-St. John’s, 2014, 1–12. IEEE.
   Google Scholar
• Hurtós, N., N. Palomeras, A. Carrera, and M. Carreras. 2017. Autonomous detection, following and mapping of an underwater chain using sonar. Ocean Engineering 130:336–50.
   Web of Science ®Google Scholar
• Karabchevsky, S., D. Kahana, O. Ben-Harush, and H. Guterman. 2011. FPGA-based adaptive speckle suppression filter for underwater imaging sonar. IEEE Journal of Oceanic Engineering 36 (4):646–57.
   Web of Science ®Google Scholar
• Lee, Y., J. Choi, N. Y. Ko, and H. T. Choi. 2017. Probability-based recognition framework for underwater landmarks using sonar images. Sensors 17 (9):1953.
   PubMed Web of Science ®Google Scholar
• Leier, S., R. Fandos, and A. M. Zoubir. 2015. Motion Error Influence on Segmentation and Classification Performance in SAS-Based Automatic Mine Countermeasures. IEEE Journal of Oceanic Engineering 40 (1):57–70.
   Web of Science ®Google Scholar
• MATLAB. 2013. R2013a. Natick, Massachusetts: The MathWorks Inc.
   Google Scholar
• Medina-Carnicer, R., A. Carmona-Poyato, R. Muñoz-Salinas, and F. J. Madrid-Cuevas. 2010. Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of thresholding methods. IEEE Transactions on Image Processing 19 (1):165–73.
   PubMed Web of Science ®Google Scholar
• Melo, J., and A. Matos. 2017. Survey on advances on terrain based navigation for autonomous underwater vehicles. Ocean Engineering 139:250–64.
   Web of Science ®Google Scholar
• Myers, V., I. Quidu, B. Zerr, T. O. Sabo, and R. E. Hansen. 2019. Synthetic aperture sonar track registration with motion compensation for coherent change detection. IEEE Journal of Oceanic Engineering :1–8.
   Web of Science ®Google Scholar
• Pan, X., Chen, Q., Xu, W., LI, J., Sun, F. 2016. Shallow-water wideband low-frequency synthetic aperture sonar for an autonomous underwater vehicle. Ocean Engineering, 118, 117–129. https://doi.org/10.1016/j.oceaneng.2016.03.063
   Google Scholar
• Parker, J. R. 1997. Algorithms for image processing and computer vision, 417. New York: John Wiley & Sons, Inc.
   Google Scholar
• Petillot, Y. R., S. R. Reed, and J. M. Bell. 2002. Real time AUV pipeline detection and tracking using side scan sonar and multi-beam echo-sounder. In: OCEANS’02 MTS/IEEE, 217–222, IEEE.
   Google Scholar
• Sternlicht, D., and J. F. Pesaturo. 2004. Synthetic aperture sonar: Frontiers in underwater imaging. Sea Technology, 45:27–34.
   Web of Science ®Google Scholar
• Tso, B., and P. Mather. 2009. Classification methods for remotely sensed data. 2nd ed. Boca Raton: CRC press.
   Google Scholar
• Vale, G. M. d., and A. P. Dal Poz. 2002. Processo de detecção de bordas de canny. Boletim de Ciências Geodésicas 8 (2):67–78.
   Google Scholar
• Vinutha, K. V., K. Vinutha, and T. Yuvaraja. 2018. Under Water Mine Detection Using Sonar. Journal of Computational and Theoretical Nanoscience 15 (6):2150–2.
   Google Scholar
• Williams, D. P. 2011. On adaptive underwater object detection. In: 2011 IEEE/J International Conference on Intelligent Robots and Systems, 4741–8, IEEE.
   Google Scholar
• Yitzhaky, Y., and E. Peli. 2003. A method for objective edge detection evaluation and detector parameter selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (8):1027–33.
   Web of Science ®Google Scholar
• Zhang, X., and S. Du. 2015. A linear dirichlet mixture model for decomposing scenes: Application to analyzing urban functional zonings. Remote Sensing of Environment, 169:37–49.
   Web of Science ®Google Scholar