Automatic benthic imagery recognition using a hierarchical two-stage approach

Abstract

The main objective of this work is to establish an automated classification system of seabed images. A novel two-stage approach to solving the image region classification task is presented. The first stage is based on information characterizing geometry, colour and texture of the region being analysed. Random forests and support vector machines are considered as classifiers in this work. In the second stage, additional information characterizing image regions surrounding the region being analysed is used. The reliability of decisions made in the first stage regarding the surrounding regions is taken into account when constructing a feature vector for the second stage. The proposed technique was tested in an image region recognition task including five benthic classes: red algae, sponge, sand, lithothamnium and kelp. The task was solved with the average accuracy of 90.11% using a data set consisting of 4589 image regions and the tenfold cross-validation to assess the performance. The two-stage approach allowed increasing the classification accuracy for all the five classes, more than 27% for the “difficult” to recognize “kelp” class.

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References

  1. 1.

    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-theart superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  2. 2.

    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  3. 3.

    Celebi, A.T., Erturk, S.: Visual enhancement of underwater images using empirical mode decomposition. Expert Syst. Appl. 39(1), 800–805 (2012)

    Article  Google Scholar 

  4. 4.

    Cernadas, E., Fernndez-Delgado, M., Gonzlez-Rufino, E., Carrin, P.: Influence of normalization and color space to color texture classification. Pattern Recognit. 61, 120–138 (2017)

    Article  Google Scholar 

  5. 5.

    Cherouat, S., Soltani, F., Schmitt, F., Daout, F.: Using fractal dimension to target detection in bistatic SAR data. SIViP 9(2), 365–371 (2015)

    Article  Google Scholar 

  6. 6.

    Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)

    Article  Google Scholar 

  7. 7.

    Galdran, A., Pardo, D., Picn, A., Alvarez-Gila, A.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 26, 132–145 (2015)

    Article  Google Scholar 

  8. 8.

    Galloway, W.E.: Process framework for describing the morphologic and stratigraphic evolution of deltaic depositional systems. Deltas: Models for Exploration, pp. 87–98 (1975)

  9. 9.

    Ghani, A.S.A., Isa, N.A.M.: Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl. Soft Comput. 27, 219–230 (2015)

    Article  Google Scholar 

  10. 10.

    Gleason, A.C.R., Reid, R.P., Voss, K.J.: Automated classification of underwater multispectral imagery for coral reef monitoring. OCEANS 2007, 1–8 (2007)

    Google Scholar 

  11. 11.

    Gobi, A.F.: Towards generalized benthic species recognition and quantification using computer vision. In: OCEANS 2010, pp. 1–6. IEEE, Sydney (2010)

  12. 12.

    Haralick, R., Shanmugam, K.: Computer classification of reservoir sandstones. IEEE Trans. Geosci. Electron. 11(4), 171–177 (1973)

    Article  Google Scholar 

  13. 13.

    Mansoor, H., Sorayya, M., Aishah, S., Mosleh, M.A.: Automatic recognition system for some cyanobacteria using image processing techniques and ANN approach. In: International Conference on Environmental and Computer Science, vol. 19, pp. 73–78. Singapore (2011)

  14. 14.

    Hamilton, L.J.: Topics in acoustic seabed segmentation current practice, open software, and data fusion. In: Proceedings of International Conference Acoustics, Development, and the Environment. Fremantle (2012)

  15. 15.

    Jalali, S., Seekings, P.J., Tan, C., Tan, H.Z.W., Lim, J.H., Taylor, E.A.: Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2013)

  16. 16.

    Jerosch, K., Ldtke, A., Schlter, M., Ioannidis, G.: Automatic content-based analysis of georeferenced image data: detection of beggiatoa mats in seafloor video mosaics from the Hkon Mosby Mud Volcano. Comput. Geosci. 33(2), 202–218 (2007)

    Article  Google Scholar 

  17. 17.

    Kamarainen, J.K., Kyrki, V., Kalviainen, H.: Invariance properties of gabor filter-based features-overview and applications. IEEE Trans. Image Process. 15(5), 1088–1099 (2006)

    Article  Google Scholar 

  18. 18.

    Kaplan, N.H., Ayten, K.K., Dumlu, A.: Single image dehazing based on multiscale product prior and application to vision control. SIViP 11(8), 1389–1396 (2017)

    Article  Google Scholar 

  19. 19.

    Ludtke, A., Jerosch, K., Herzog, O., Schlter, M.: Development of a machine learning technique for automatic analysis of seafloor image data: case example, pogonophora coverage at mud volcanoes. Comput. Geosci. 39, 120–128 (2012)

    Article  Google Scholar 

  20. 20.

    Manderson, T., Li, J., Cort’es Poza, D., Dudek, N., Meger, D., Dudek, G.: Field and Service Robotics: Results of the 10th International Conference, chap. Towards Autonomous Robotic Coral Reef Health Assessment, pp. 95–108. Springer International Publishing, Cham (2016)

  21. 21.

    Mosleh, M.A., Manssor, H., Malek, S., Milow, P., Salleh, A.: A preliminary study on automated freshwater algae recognition and classification system. BMC Bioinform. 13(17), 1–13 (2012)

    Google Scholar 

  22. 22.

    Pugh, M., Tiddeman, B., Dee, H., Hughes, P.: Towards automated classification of seabed substrates in underwater video. In: 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), pp. 9–16 (2014)

  23. 23.

    Qin, C., Song, S., Huang, G., Zhu, L.: Unsupervised neighborhood component analysis for clustering. Neurocomputing 168, 609–617 (2015)

    Article  Google Scholar 

  24. 24.

    Šaškov, A., Dahlgren, T.G., Rzhanov, Y., Schläppy, M.L.: Comparison of manual and semi-automatic underwater imagery analyses for monitoring of benthic hard-bottom organisms at offshore renewable energy installations. Hydrobiologia 756(1), 139–153 (2014)

    Google Scholar 

  25. 25.

    Schwartz, W.R., Roberti de Siqueira, F., Pedrini, H.: Evaluation of feature descriptors for texture classification. J. Electron. Imaging 21(2), 023,016–1–023,016–17 (2012)

    Article  Google Scholar 

  26. 26.

    Shihavuddin, A., Gracias, N., Garcia, R., Escartin, J., Birger Pedersen, R.: Automated classification and thematic mapping of bacterial mats in the north sea. In: OCEANS-Bergen, 2013 MTS/IEEE, pp. 1–8 (2013)

  27. 27.

    Soh, L.K., Tsatsoulis, C.: Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)

    Article  Google Scholar 

  28. 28.

    Vapnik, V.N.: Statistical Learning Theory. Wiley, Hoboken (1998)

    Google Scholar 

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Acknowledgements

The authors would like to thank Sergej Olenin and his team for allowing to use their video sequences and manual data labelling.

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Correspondence to Tadas Rimavičius.

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Rimavičius, T., Gelžinis, A., Verikas, A. et al. Automatic benthic imagery recognition using a hierarchical two-stage approach. SIViP 12, 1107–1114 (2018). https://doi.org/10.1007/s11760-018-1262-4

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Keywords

  • Seabed image segmentation
  • Machine learning
  • Supervised classification
  • Feature extraction
  • Two-stage classifier