Abstract
To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Brink A (1992) Thresholding of digital images using two-dimensional entropies. Pattern Recogn 25(8):803–808. doi:10.1016/0031-3203(92)90034-G
Chang L, Chong-xu Y (2012) Image segmentation based on maximum entropy and kernel self-organizing map. In: 2012 spring congress on engineering and technology (S-CET). IEEE, pp 1–4. doi:10.1109/SCET.2012.6341976
Chavez PS Jr, Isbrecht J, Galanis P, Gabel GL, Sides SC, Soltesz DL, Ross SL, Velasco MG (2002) Processing, mosaicking and management of the Monterey Bay digital sidescan-sonar images. Mar Geol 181(1):305–315. doi:10.1016/S0025-3227(01)00273-0
De Beukelaer S, MacDonald I, Guinnasso N Jr, Murray J (2003) Distinct side-scan sonar, RADARSAT SAR, and acoustic profiler signatures of gas and oil seeps on the Gulf of Mexico slope. Geo-Mar Lett 23(3–4):177–186. doi:10.1007/s00367-003-0139-9
Dorschel B, Wheeler A, Huvenne V, De Haas H (2009) Cold-water coral mounds in an erosive environmental setting: TOBI side-scan sonar data and ROV video footage from the northwest Porcupine Bank, NE Atlantic. Mar Geol 264(3):218–229. doi:10.1016/j.margeo.2009.06.005
Gong M, Su L, Jia M, Chen W (2014) Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans Fuzzy Syst 22(1):98–109
Goswami R, Rao GSB, Rani SS, Kumar M, Prasad SD, Kumar GH (2010) Segmentation of sonar images based on adaptive thresholding with image histogram. Digit Image Process 2(3):89–95
Gueguen L, Velasco-Forero S, Soille P (2014) Local mutual information for dissimilarity-based image segmentation. J Math Imaging Vis 48(3):625–644. doi:10.1007/s10851-013-0432-9
Hanbay K, Talu MF (2014) Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set. Appl Soft Comput 21:433–443. doi:10.1016/j.asoc.2014.04.008
Kaur A, Singh M (2012) An overview of pso-based approaches in image segmentation. Int J Eng Technol 2(8):1349–1357
Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: IEEE international conference on evolutionary computation. IEEE, pp 303–308. doi:10.1109/ICEC.1997.592326
Kohntopp D, Lehmann B, Kraus D, Birk A (2015) Segmentation and classification using active contours based superellipse fitting on side scan sonar images for marine demining. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, pp 3380–3387. doi:10.1109/ICRA.2015.7139666
Krstinić D, Skelin AK, Slapničar I (2011) Fast two-step histogram-based image segmentation. IET Image Proc 5(1):63–72. doi:10.1049/iet-ipr.2009.0107
Kumagai H, Tsukioka S, Yamamoto H, Tsuji T, Shitashima K, Asada M, Yamamoto F, Kinoshita M (2010) Hydrothermal plumes imaged by high-resolution side-scan sonar on a cruising AUV, Urashima. Geochem Geophys Geosyst. doi:10.1029/2010GC003337
Lianantonakis M, Petillot YR (2005) Sidescan sonar segmentation using active contours and level set methods. In: Oceans 2005-Europe. IEEE, pp 719–724. doi: 10.1109/OCEANSE.2005.1511803
Mignotte M, Collet C, Perez P, Bouthemy P (2000) Sonar image segmentation using an unsupervised hierarchical MRF model. IEEE Trans Image Process 9(7):1216–1231. doi:10.1109/83.847834
Powers DM (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Tech 2(1):37–63. doi:10.9735/2229-3981
Reed S, Petillot Y, Bell J (2003) An automatic approach to the detection and extraction of mine features in sidescan sonar. IEEE J Ocean Eng 28(1):90–105. doi:10.1109/JOE.2002.808199
Sagawa T, Mikami A, Komatsu T, Kosaka N, Kosako A, Miyazaki S, Takahashi M (2008) Technical Note. Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study. Int J Remote Sens 29(1):281–291. doi:10.1080/01431160701269028
Sarkar S, Das S (2013) Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—a differential evolution approach. IEEE Trans Image Process 22(12):4788–4797. doi:10.1109/TIP.2013.2277832
Sengur A, Guo Y (2011) Color texture image segmentation based on neutrosophic set and wavelet transformation. Comput Vis Image Underst 115(8):1134–1144. doi:10.1016/j.cviu.2011.04.001
Stitt J, Tutwiler R, Lewis A (2001) Fuzzy c-means image segmentation of side-scan sonar images. In: SIP. pp 27–32
Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation
Wang X, Wang H, Ye X, Zhao L, Wang K (2007) A novel segmentation algorithm for side-scan sonar imagery with multi-object. In: IEEE international conference on robotics and biomimetics, 2007. ROBIO 2007. IEEE, pp 2110–2114. doi:10.1109/ROBIO.2007.4522495
Acknowledgments
The research is supported by National Natural Science Foundation of China (Coded by 41576107, 41376109 and 41176068). The constructive comments from both the two anonymous reviewers and the co-editor-in-chief Roger Urgeles help us to revise this paper, we greatly appreciate them.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhao, J., Wang, X., Zhang, H. et al. Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm. Mar Geophys Res 37, 229–241 (2016). https://doi.org/10.1007/s11001-016-9276-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11001-016-9276-1