International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Deepika Selvaraj1, Elangovan Krishnamurthy2, Don Sasikumar3, Lekshmi Sivankutty4, Ganesan Kaliyaperumal1*

1 Vellore Institute of Technology, Vellore, Tamil Nadu, India

2 Sri Annai Polytechnic College, Vellore, Tamil Nadu, India

3 Department of computer science and engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India

4 Naval Physical & Oceanographic Laboratory, Defence Research and Development Organisation, Kerala, India


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The satellite PALSAR-2/ALOS-2 image plays a vital role in marine environment protection, weather analysis, and other geophysical processes. Satellite image processing is requiring more time to extract the different features in the ocean region. In this article, the proposed tile-based iteration technique along with line transform for high-speed ship detection and wakes feature extraction in the PALSAR-2/ALOS-2 image. The standard noise removal/despeckling techniques are applied to the SAR data to get accurate information about the ship for detection and wake features for velocity estimation. The despeckling performance is evaluated by the metrics called Edge Preservation Index (EPI) and Structure Similarity Index (SSIM) metric. Furthermore, the proposed tiling and region-based morphological operation are applied to the despeckled SAR image for ship detection and the results are compared with an exciting method. Line-based Radon and Hough transform has estimated the angle and wavelength of the ship wake lines which are present behind the ship. The article is focused on rapid ship detection and feature extraction which consumes only 160 seconds to process the tile-based combination of image size 1978 × 2536, 7 times faster than without tilling algorithm. To demonstrate the proposed work, the test environment is created using MATLAB and the result proves that 1.0x to 1.5x faster than the standard existing method.

Keywords: Feature extraction, Ship detection, Speckle noise, Synthetic aperture radar, Tiling.

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  1. Ahmed, S.M., Eldin, F.A.E., Tarek, A.M. 2010. Speckle noise reduction in SAR images using adaptive morphological filter. In 2010 10th International Conference on Intelligent Systems Design and Applications. 260–265.

  2. Arshad, N., Moon, K.S., Kim, J.N. 2014. Adaptive real-time ship detection and tracking using morphological operations. Journal of information and communication convergence engineering, 12, 168–172.

  3. Bartyzel, K. 2016. Adaptive kuwahara filter. Signal, Image and Video Processing, 10, 663–670.

  4. Bi, F., Zhu, B., Gao, L., Bian, M. 2012. A visual search inspired computational model for ship detection in optical satellite images. IEEE Geoscience and Remote Sensing Letters, 9, 749–753.

  5. Chaturvedi, S.K. 2019. Study of synthetic aperture radar and automatic identification system for ship target detection. Journal of Ocean Engineering and Science, 4, 173–182.

  6. Chumning, H., Huadong, G., Changlin, W. 2002. Edge preservation evaluation of digital speckle filters. In IEEE International Geoscience and Remote Sensing Symposium. 2471–2473.

  7. Corbane, C., Najman, L., Pecoul, E., Demagistri, L., Petit, M. 2010. A complete processing chain for ship detection using optical satellite imagery. International Journal of Remote Sensing, 31, 5837–5854.

  8. Dingle Robertson, L., Davidson, A.M., McNairn, H., Hosseini, M., Mitchell, S., de Abelleyra, D., Verón, S., Le Maire, G., Plannells, M., Valero, S., Ahmadian, N. 2020. C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems. International Journal of Remote Sensing, 41, 9628–9649.

  9. Graziano, M.D., D’Errico, M., Rufino, G. 2016. Ship heading and velocity analysis by wake detection in SAR images. Acta astronautica, 128, 72–82.

  10. Guo, J.H., Yang, F., Tan, H., Wang, J.X., Liu, Z.H. 2016. Image matching using structural similarity and geometric constraint approaches on remote sensing images. Journal of Applied Remote Sensing, 10, 045007.

  11. Iervolino, P., Guida, R., Whittaker, P. 2015. A novel ship-detection technique for Sentinel-1 SAR data. In 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). 797–801.

  12. Jiaqiu, A., Xiangyang, Q., Weidong, Y., Yunkai, D., Fan, L., Li, S., Yafei, J. 2011. A novel ship wake CFAR detection algorithm based on SCR enhancement and normalized Hough transform. IEEE Geoscience and Remote Sensing Letters, 8, 681–685.

  13. Jin, Y.Q., Wang, S.Q. 2000. An algorithm for ship wake detection from the synthetic aperture radar images using the Radon transform and morphological image processing. The Imaging Science Journal, 48, 159–163.

  14. Jose, F.J., Rajesh, M., Rajan, V. 2021. Development of wake detection and analysis by using image processıng. In Computer Networks and Inventive Communication Technologies (519–529). Springer, Singapore.

  15. Joseph, S.I.T., Sasikala, J., Juliet, D.S. 2020. Detection of ship from satellite images using deep convolutional neural networks with improved median filter. In Artificial Intelligence Techniques for Satellite Image Analysis. 69–82.

  16. Kanoun, B., Ferraioli, G., Pascazio, V., Schirinzi, G. 2018. Fast algorithm for despeckling sentinel-1 SAR data. In EUSAR 2018; 12th European Conference on Synthetic Aperture Radar. 1–5.

  17. Li, W., He, M., Zhang, S. 2009. A heterogeneity-based ship detection algorithm for SAR imagery. In 2009 2nd International Congress on Image and Signal Processing. 1–5.

  18. Lu, Y., Zhuang, X., Sun, Z., Wang, S., Liu, W. 2017. Wavelength estimation method based on radon transform and image texture. Journal of Shipping and Ocean Engineering, 7, 186–191.

  19. Ma, J., Zou, C., Jin, X. 2017. An improved image enhancement algorithm. Wuhan University Journal of Natural Sciences, 22, 85–92.

  20. Nie, T., Han, X., He, B., Li, X., Liu, H., Bi, G. 2020. Ship detection in panchromatic optical remote sensing images based on visual saliency and multi-dimensional feature description. Remote Sensing, 12,152.

  21. Nilsson, M., Dahl, M., Claesson, I. 2005. The successive mean quantization transform. In Proceedings. (ICASSP'05). IEEE International Conference on Acoustics, Speech, and Signal Processing. iv–429.

  22. Proia, N., Pagé, V. 2009. Characterization of a Bayesian ship detection method in optical satellite images. IEEE Geoscience and Remote Sensing Letters, 7, 226–230.

  23. Rui, T.J., Isa, N.A.M. 2016. Intensity exposure-based bi-histogram equalization for image enhancement. Turkish Journal of Electrical Engineering and Computer Science, 24, 3564–3585.

  24. Saleh, B., Rawashdeh, S. 2007. Study of urban expansion in jordanian cities using GIS and remoth Sensing. International Journal of Applied Science and Engineering, 5, 41–52.

  25. Schmitt, A. 2016. Multiscale and multidirectional multilooking for SAR image enhancement. IEEE Transactions on Geoscience and Remote Sensing, 54, 5117–5134.

  26. Shimada, M. 2013. ALOS-2 science program. In 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. 2400–2403.

  27. Subramani, B., Veluchamy, M. 2018. MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization. International Journal of Imaging Systems and Technology, 28, 217–222.

  28. Sumantyo, J.T.S., Amini, J. 2008. A model for removal of speckle noise in SAR images (ALOS PALSAR). Canadian Journal of Remote Sensing, 34, 503–515.

  29. Tian, M., Yang, Z., Mao, Z., Liao, G. 2019. Ship wake region detection by using multi‐feature recombination and area‐based morphological analysis in ATI‐SAR systems. The Journal of Engineering, 7397–7402.

  30. Tings, B., Velotto, D. 2018. Comparison of ship wake detectability on C-band and X-band SAR. International Journal of Remote Sensing, 39, 4451–4468.

  31. Van Ginkel, M., Hendriks, C.L., Van Vliet, L.J. 2004. A short introduction to the Radon and Hough transforms and how they relate to each other. Delft University of Technology.

  32. Wang, R., Li, S., Kuruoglu, E.E. 2013. A novel algorithm for image denoising based on unscented Kalman filtering. International Journal of Information and Communication Technology, 5, 343–353.

  33. Xu, Z., Tang, B., Cheng, S. 2018. Faint ship wake detection in PolSAR images. IEEE Geoscience and Remote Sensing Letters, 15, 1055–1059.

  34. Yadav, V.P., Prasad, R., Bala, R., Srivastava, P.K. 2020. Synergy of vegetation and soil microwave scattering model for leaf area index retrieval using C-band sentinel-1A satellite data. IEEE Geoscience and Remote Sensing Letters.

  35. Yang, G., Li, B., Ji, S., Gao, F., Xu, Q. 2013. Ship detection from optical satellite images based on sea surface analysis. IEEE Geoscience and Remote Sensing Letters, 11, 641–645.

  36. Zilman, G., Zapolski, A., Marom, M. 2004. The speed and beam of a ship from its wake's SAR images. IEEE Transactions on Geoscience and Remote Sensing, 42, 2335–2343.


Received: 2021-06-24
Revised: 2021-08-08
Accepted: 2021-10-14
Available Online: 2022-03-01

Cite this article:

Selvaraj, Deepika, Elangovan Krishnamurthy, Don Sasikumar, Lekshmi Sivankutty, Ganesan Kaliyaperumal. 2022. Tiling algorithm with line-based transform for rapid ship detection and wake feature extraction in ALOS-2 SAR sensor data. International Journal of Applied Science and Engineering, 19, 2021126.

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


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