An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery
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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.
KeywordsParticle swarm optimization Simple linear clustering Image processing Shoreline extraction
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.
- 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.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.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
- 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.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 4–6 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.
- 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.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.CrossRefGoogle 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
- 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.
- 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.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.Google Scholar
- 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.
- 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.