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An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery

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Abstract

This study presents an extended shoreline detection approach from pansharpened images of Turkish RASAT satellite which covers red, green and blue bands of the optical spectrum with 15 m ground resolution and panchromatic band with 7.5 m spatial resolution. The Lake Ercek of Turkey has been selected as the study area, which is a tectonic lake and home to a variety of water birds. The satellite images of the lake taken in 2013 and 2014 were considered for analysis. The proposed shoreline extraction system consists of a sequence of image processing steps in which simple linear clustering (SLIC) and particle swarm optimization (PSO) are the main components. SLIC was used to create superpixels that form basis for object-based image analysis while PSO was employed for classifying objects into corresponding classes. The resulting images still contained unwanted artefacts; therefore, a post-processing step was performed to improve the accuracy of segmentation by applying thresholding, morphological processing, and manual editing for noise removal. The proposed framework was applied on two temporal RASAT images to test the variations of defined parameter settings. The success of the proposed system was to obtain shorelines with satisfying accuracy without using NIR band. Finally, the extracted shorelines were vectorised and compared with manually digitized shorelines from pansharpened satellite images for accuracy assessment.

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References

  • Acar, U., Bayram, B., Balik Sanli, F., Abdikan, S., Sunar, F., & Cetin H. I. (2012). An algorithm for coastline detection using SAR images. In International archives of the photogrammetry, remote sensing and spatial ınformation sciences, ICWG III/VII, Volume XXXIX-B3, 2012, XXII ISPRS Congress, 25 August–01 September 2012, Melbourne, Australia, pp. 457–460.

  • Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282.

    Article  Google Scholar 

  • Addo, K. A. (2013). Shoreline morphological changes and the human factor. Case study of Accra Ghana. Journal of Coastal Conservation, 17(1), 85–91.

    Article  Google Scholar 

  • Aedla, R., Dwarakish, G. S., & Reddy, V. (2015). Automatic shoreline detection and change detection analysis of Netravati-Gurpur River Mouth using histogram equalization and adaptive thresholding techniques. Aquatic Procedia, 4, 563–570.

    Article  Google Scholar 

  • Bayram, B., Acar, U., Seker, Z. D., & Ari, A. (2008). A novel algorithm for coast line fitting through a case study over bosphorus. Journal of Coastal Research, 24(4), 983–999.

    Article  Google Scholar 

  • Bayram, B., Avşar, Ö., Şeker, D. Z., Kayi, A., Erdogan, M., & Eker, O. (2017). The role of national and international geospatial data sources in coastal zone management. Fresenius Environmental Bulletin (FEB), 26, 383–391.

    Google Scholar 

  • Bayram, B., Demir, N., Ogurlu, M., Catal Reis, H., & Seker, D. Z. (2016). 3D Shoreline extraction using orthopoto-maps and LIDAR. In 37th Asian conference on remote sensing, 17–21 October 2016, Sri Lanka, Colombo, tS38, AB0594, pp. 1–5.

  • Bayram, B., Janpaule, I., Avsar, O., Ogurlu, M., Bozkurt, S., Catal Reis, H., et al. (2015). Shoreline extraction and change detection using 1:5000 scale orthophoto maps: A case study of Latvia-Riga. International Journal of Environment and Geoinformatics, 2(3), 1–6.

    Article  Google Scholar 

  • Bayram, B., Seker, D. Z., Acar, U., Yuksel, Y., Guner, H. A. A., & Cetin, I. (2013). An integrated approach to temporal monitoring of the shoreline and basin of Terkos Lake. Journal of Coastal Research, 29(6), 1427–1435.

    Article  Google Scholar 

  • Bouchahma, M., & Yan, W. (2012). Automatic measurement of shoreline change on Djerba Island of Tunisia. Computer and Information Science, 5(5), 17–24.

    Article  Google Scholar 

  • Couceiro, M. S. (2012). https://www.mathworks.com/matlabcentral/fileexchange/29517-segmentation. Accessed November 6, 2016.

  • Ding, X., & Li, X. (2014). Coastline detection in SAR images using multiscale normalized cut segmentation. In Geoscience and remote sensing symposium (IGARSS), 2014 IEEE ınternational. https://doi.org/10.1109/igarss.2014.6947478.

  • Duman, N., & Cicek, I. (2012). The geomorphology of Lake Erçek Basin and the formation of the lake. Journal of International Social Research, 5(20), 246–260.

    Google Scholar 

  • Erdogan, M., Yilmaz, A., & Eker, O. (2016). The georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy. Geocarto International, 31(6), 647–660. https://doi.org/10.1080/10106049.2015.1073367.

    Article  Google Scholar 

  • Gammal, J., Norkko, J., Pilditch, C. A., & Norkko, A. (2016). Coastal hypoxia and the importance of benthic macrofauna communities for ecosystem functioning. Estuaries and Coasts. https://doi.org/10.1007/s12237-016-0152-7.

    Google Scholar 

  • Gao, H., Birkett, C., & Lettenmaier, D. P. (2012). Global monitoring of large reservoir storage from satellite remote sensing. Water Resources Research. https://doi.org/10.1029/2012WR012063.

    Google Scholar 

  • Gens, R. (2010). Remote sensing of coastlines: Detection, extraction and monitoring. International Journal of Remote Sensing, 31(7), 1819–1836.

    Article  Google Scholar 

  • Ghamisi, P., Couceiro, M. S., Benediktsson, J. A., & Ferreira Nuno, M. F. (2012). An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Systems with Applications, 39, 12407–12417.

    Article  Google Scholar 

  • Ghosh, M. K., Kumar, L., & Roya, C. (2015). Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 137–144.

    Article  Google Scholar 

  • Guariglia, A., Buonamassa, A., Losurdo, A., Saladino, R., Trivigno, M. L., Zaccagnino, A., et al. (2006). A multisource approach for coastline mapping & identification of the shoreline changes. Annals of Geophysics, 49(1), 295–304.

    Google Scholar 

  • Hu, Z., Gong, H. & Zhu, L. (2007). Fast flooding information extraction in emergency response of flood disaster. In Proceedings of the ISPRS workshop on updating geo-spatial databases with ımagery and the 5th ISPRS workshop on DMGISs, Urumchi, 28–29 August 2007, pp.173–177.

  • Jawak, S. D., Kulkarni, K., & Luis, A. J. (2015). A review on extraction of lakes from remotely sensed optical satellite data with a special focus on Cryospheric Lakes. Advances in Remote Sensing, 4, 196–213.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). A new optimizer using particle swarm theory. In Micro machine and human science, 1995. MHS ‘95, proceedings of the sixth ınternational symposium on 46 October 1995, pp. 39–43. https://doi.org/10.1109/mhs.1995.494215.

  • Klinger, T., Ziems, M., Heipke, C., Schenke, H. W., & Ott, N. (2010). Antarctic coastline detection using snakes. In A special joint symposium of ISPRS technical commission IV & AutoCarto in conjunction with ASPRS/CaGIS 2010 fall specialty conference, November 15–19, 2010 Orlando, Florida. http://www.isprs.org/proceedings/XXXVIII/part4/files/Klinger.pdf. Accessed January 4, 2016.

  • Krek, A., Stont, Z., & Ulyanova, M. (2016). Alongshore bed load transport in the southeastern part of the Baltic Sea under changing hydrometeorological conditions: Recent decadal data. Regional Studies in Marine Science, 7, 81–87.

    Article  Google Scholar 

  • Li, R., Di, K., & Ma, R. (2001). A comparative study of shoreline mapping techniques. In 4th ınternational symposium on computer mapping and GIS for coastal zone management, 18–20 June 2001, Halifax, Nova Scotia, Canada.

  • Li, L., & Li, D. (2008). Fuzzy entropy image segmentation based on particle swarm optimization. Progress in Natural Science, 18(9), 1167–1171. https://doi.org/10.1016/j.pnsc.2008.03.020.

    Article  Google Scholar 

  • Liu, H., Sherman, D., & Gu, S. (2007). Automated extraction of shorelines from airborne light detection and ranging data and accuracy assessment based on Monte Carlo simulation. Journal of Coastal Research, 23, 1359–1369.

    Article  Google Scholar 

  • Machado, C. A. S., Beltrame, A. M. K., Shinohara, E. J., Giannotti, M. A., Durieux, L., Nobrega, T. M. Q., et al. (2014). Identifying concentrated areas of trip generators from high spatial resolution satellite images using object-based classification techniques. Applied Geography, 53, 271–283.

    Article  Google Scholar 

  • MathWorks, R2016b Documentation. https://www.mathworks.com/help/images/ref/regionprops.html. Accessed January 1, 2016.

  • Ministry of Culture and Tourism, Provincial Directorate. http://www.vankulturturizm.gov.tr/TR,88261/ercek-golu-kus-cenneti.html. Accessed January 4, 2016.

  • Mishra, D., Bose, I., De Utpal, C., & Pradhan, B. (2014). A multilevel image thresholding using particle swarm optimization. International Journal of Engineering and Technology, 6(2), 1204–1211.

    Google Scholar 

  • Mohsen, F., Hadhoud, M., Mostafa, K., & Amin, K. (2012). A new image segmentation method based on particle swarm optimization. The International Arab Journal of Information Technology, 9(5), 487–493.

    Google Scholar 

  • Papa, F., Prigent, C., & Rossow, W. B. (2008). Monitoring flood and discharge variations in the Large Siberian Rivers from a multi-satellite technique. Surveys In Geophysics, 29, 297–317. https://doi.org/10.1007/s10712-008-9036-0.

    Article  Google Scholar 

  • Paravolidakis, V., Moirogiorgou, K., Ragia, L., Zervakis, M., & Synolakis, C. (2016). Coastline extraction from aerial images based on edge detection. In ISPRS annals of the photogrammetry, remote sensing and spatial ınformation sciences, Volume III-8, XXIII ISPRS congress, 12–19 July 2016, Prague, Czech Republic, pp. 153–158. https://doi.org/10.5194/isprsannals-iii-8-153-2016.

  • Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., & Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM + multi-temporal images with subpixel precision. Remote Sensing of Environment, 123(2012), 1–11.

    Article  Google Scholar 

  • Purkis, S. J., Gardiner, R., Johnston, M. W., & Sheppard, C. R. C. (2016). A half-century of coastline change in Diego Garcia—The largest atoll island in the Chagos. Geomorphology, 261, 282–298.

    Article  Google Scholar 

  • Rini, D. P., Shamsuddin, S. M., & Yuhaniz, S. S. (2011). Particle swarm optimization: Technique, system and challenges. International Journal of Computer Applications (0975–8887), 14(1), 19–27.

    Article  Google Scholar 

  • Schmitt, M., Wei, L., & Zhu, X. X. (2015). Automatic coastline detection in non-locally filtered TANDEM-X data. In Proceedings of IEEE ınternational geoscience and remote sensing symposium (IGARSS) 2015, IEEE Xplore. IGARSS 2015, 26.–31. Jul. 2015, Mailand, Italien, pp. 1036–1039.

  • Wang, X. Z., Zhang, H. G., Fu, B., & Shi, A. (2013). Analysis on the coastline change and erosion-accretion evolution of the Pearl River Estuary, China, based on remote-sensing images and nautical charts. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.JRS.7.073519.

    Google Scholar 

  • Wu, H., Liu, C., Zhang, Y., & Sun, W. (2009). Water feature extraction from aerial-image fused with airborne lidar data. In IEEE urban remote sensing event, 2009 joint, pp. 1–7.

  • Yang, C., He, R., & Wang, S. (2008). Extracting waterbody from Beijing-1 micro-satellite images based on knowledge discovery. In Proceedings of the IEEE ınternational geoscience and remote sensing symposium, Boston, 7–11, July 2008, pp. 850–853. https://doi.org/10.1109/igarss.2008.4779856.

  • Yousef, A., & Iftekharuddin, K. (2014). Shoreline extraction from the fusion of LiDAR DEM data and aerial images using mutual information and genetic algorithms. In International joint conference on neural networks (IJCNN), pp.1007–1014. https://doi.org/10.1109/ijcnn.2014.6889863.

  • Yu, S., Mou, Y., Xu, D., You, X., Zhou, L., & Zeng, W. (2013). A new algorithm for shoreline extraction from satellite imagery with non-separable wavelet and level set method. International Journal of Machine Learning and Computing, 3(1), 158–163.

    Article  Google Scholar 

  • Zheng, G., Peng, L., Tao, G., & Wang, C. (2011). Remote sensing analysis of Bohai Bay West Coast shoreline changes. In spatial data mining and geographical knowledge services (ICSDM), 2011 IEEE ınternational conference on 29 June–1 July 2011, pp. 549–552. https://doi.org/10.1109/icsdm.2011.5969106.

  • Zhang, H., Zhang, B., Guo, H., Lu, J., & He, H. (2013). An automatic coastline extraction method based on active contour model. In Geoinformatics (GEOINFORMATICS), 2013 21st ınternational conference on 20-22 June 2013. https://doi.org/10.1109/geoinformatics.2013.6626130.

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Acknowledgements

This study presents the preliminary results of the algorithms which will also be used in the project entitled “Automatic 3D Shoreline Extraction and Analysis from UAV and UAV-LIDAR Data for Sustainable Shoreline Monitoring: Case Study of Terkos (Istanbul)” which is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with Project Number 115Y718.

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Correspondence to Hatice Catal Reis.

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Catal Reis, H., Bayram, B., Bozkurt, S. et al. An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery. J Indian Soc Remote Sens 46, 1223–1232 (2018). https://doi.org/10.1007/s12524-018-0804-0

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