Abstract

This study aims to detect a shoreline location and its changes automatically in the temporal resolution. This approach is implemented on the coastal video monitoring system applications. The proposed method applied data mining by using two main systems-a training system using classification and shoreline detection systems with Self-Organizing Map (SOM) and K-Nearest Neighbor (K-NN) algorithms. The training system performs feature texture extraction using agray-level co-occurrence matrix and the results are stored to classification process. The detection system has five processing stages: contrast stretching preprocessing and morphological contrast enhancement, SOM clustering, morphological operations, feature extraction and K-NN classification and detection shoreline. Preprocessing was used to improve the video image contrast and reliability. SOM algorithm in segmenting objects in the onshore video images. Morphological operations were applied to eliminate noise on the objects that were not needed in the spatial domain. The segmentation results of video frames classified by K-NN. The aim is to provide the class labels on each region segmentation results, namely, sea label, land label and sky label. The determination of the shoreline is done by scanning the neighboring pixels from the edge of land class label after binary image transformation. The shoreline change detection was performed by comparing the position of existing shoreline and shoreline position in the reference video frame. A Receiver Operating Characteristic (ROC) curve was used to evaluate the performance of shoreline detection systems. The results showed that the combination of SOM and K-NN was able to detect shoreline and its changes accurately.

Keywords:

Feature extraction, image enhancement, K-NN, shoreline, SOM, video monitoring,

References

1. Haralick, R.M., K. Shanmugam and T. Dinstein, 1973. Textural features for image classification. IEEE T. Syst. Man Cyb., SMC-3(6): 610-621.
   CrossRef    
2. Kim, J., B.S. Kim and S. Savarese, 2012. Comparing image classification methods: K-nearest-neighbor and support-vector-machines. Proceeding of the 6th WSEAS International Conference on Computer Engineering and Applications and American Conference on Applied Mathematics (AMERICAN-MATH'12/CEA'12). Cambridge, USA, pp: 133-138.
   
3. Aarninkhof, S.G.J., I.L. Turner, T.D.T. Dronkers, M. Caljouw and L. Nipius, 2003. A video-based technique for mapping intertidal beach bathymetry. Coastal Eng., 49(4): 275-289.
   CrossRef    
4. Al-Amri, S.S., 2011. Contrast stretching enhancement in remote sensing image. BIOINFO Sensor Netw., 1(1): 6-9.
   Direct Link
5. Almar, R., R. Ranasinghe, N. Sénéchal, P. Bonneton, D. Roelvink, K.R. Bryan, V. Marieu and J.P. Parisot, 2012. Video-based detection of shorelines at complex meso-macro tidal beaches. J. Coastal Res., 28(5): 1040-1048.
   CrossRef    
6. Argus, 2015. Argus Image Archive: Site: Egmond (Coast3D tower). Noord Holland. Retrieved from: http://argus-public.deltares.nl/archive. (Accessed on: May 1-30, 2015).
   Direct Link
7. Goncalves, R.M., J.L. Awange, C.P. Krueger, B. Heck and L. dos Santos Coelho, 2012. A comparison between three short-term shoreline prediction models. Ocean Coast. Manage., 69: 102-110.
   CrossRef    
8. Gorunescu, F., 2011. Data Mining: Concepts, Models and Techniques. Springer, Berlin, Heidelberg.
   CrossRef    
9. Han, J., M. Kamber and J. Pei, 2011. Data Mining: Concepts and Techniques. 3rd Edn., Elsevier Science, Berlington.
   Direct Link
10. Hassanpour, H., N. Samadiani and S.M.M. Salehi, 2015. Using morphological transforms to enhance the contrast of medical images. Egypt. J. Radiol. Nucl. Med., 46(2): 481-489.
    CrossRef    
11. Huang, K., S. Li, X. Kang and L. Fang, 2016. Spectral–spatial hyperspectral image classification based on KNN. Sens. Imag., 17: 1.
    CrossRef    
12. Huisman, C.E., K.R. Bryan, G. Coco and B.G. Ruessink, 2011. The use of video imagery to analyse groundwater and shoreline dynamics on a dissipative beach. Cont. Shelf Res., 31(16): 1728-1738.
    CrossRef    
13. Jain, A.K., R.P.W. Duin and J. Mao, 2000. Statistical pattern recognition: A review. IEEE T. Pattern Anal., 22(1): 4-37.
    CrossRef    
14. Kingston, K.S., B.G. Ruessink, I.M.J. Van Enckevort and M.A. Davidson, 2000. Artificial neural network correction of remotely sensed sandbar location. Mar. Geol., 169(1-2): 137-160.
    CrossRef    
15. Kohonen, T., 2013. Essentials of the self-organizing map. Neural Networks, 37: 52-65.
    CrossRef    PMid:23067803    
16. Ortiz, A., J.M. Górriz, J. Ramírez, D. Salas-González and J.M. Llamas-Elvira, 2013. Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Appl. Soft Comput., 13(15): 2668-2682.
    CrossRef    
17. Plant, N.G., S.G.J. Aarninkhof, I.L. Turner and K.S. Kingston, 2007. The performance of shoreline detection models applied to video imagery. J. Coastal Res., 23(3): 658-670.
    CrossRef    
18. Rigos, A., O.P. Andreadis, M. Andreas, M.I. Vousdoukas, G.E. Tsekouras and A. Velegrakis, 2014. Shoreline extraction from coastal images using chebyshev polynomials and RBF neural networks. Proceeding of the 10th International Conference on Artificial Intelligence Applications and Innovations. Springer, Berlin, Heidelberg, 436: 593-603.
    CrossRef    
19. Rijn, L.C.V., 2007. Manual Sediment Transport Measurements in Rivers, Estuaries and Coastal Seas. Aqua Publications, Delft, Netherlands.
    
20. Rousseeuw, P.J., 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20: 53-65.
    CrossRef    
21. Turner, I.L., V. Leyden, G. Symonds, L.A. Jackson, T. Jancar, S. Aarninkhof and I. Elshoff, 2000. Comparison of observed and predicted coastline changes at the gold coast artificial (surfing) reef. Proceeding of the 27th International Conference on Coastal Engineering. Sydney, Australia, pp: 1836-1847.
    Direct Link
22. Widyantara, I.M.O., 2015. Decoding approach with unsupervised learning of two motion fields for improving Wyner-Ziv coding of video. Int. J. Appl. Eng. Res., 10(5): 11763-11776.
    Direct Link
23. Widyantara, I.M.O., N.M.A.E.D. Wirastuti, I.M.D.P. Asana and I.B.P. Adnyana, 2016. Image enhancement using morphological contrast enhancement for video based image analysis. Proceeding of the International Conference on Data and Software Engineering (ICoDSE), Oct. 26-27.
    CrossRef    
24. Zayed, N. and H.A. Elnemr, 2015. Statistical analysis of Haralick texture features to discriminate lung abnormalities. Int. J. Biomed. Imag., 2015: 1-7.
    CrossRef    PMid:26557845 PMCid:PMC4617884    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.