WANG Changying, ZHANG Jie, SONG Pingjian. An intelligent coastline interpretation of several types of seacoasts from TM/ETM+ images based on rules[J]. Acta Oceanologica Sinica, 2014, 33(7): 89-96. doi: 10.1007/s13131-014-0482-x
Citation: WANG Changying, ZHANG Jie, SONG Pingjian. An intelligent coastline interpretation of several types of seacoasts from TM/ETM+ images based on rules[J]. Acta Oceanologica Sinica, 2014, 33(7): 89-96. doi: 10.1007/s13131-014-0482-x

An intelligent coastline interpretation of several types of seacoasts from TM/ETM+ images based on rules

doi: 10.1007/s13131-014-0482-x
  • Received Date: 2012-06-18
  • Rev Recd Date: 2013-09-27
  • A coastline is defined as the average spring tide line. Different types of seacoast, such as sandy, silty, and biological coast, have different indicators of interpretation. It is very difficult to develop a universal method for interpreting all shorelines. Therefore, the sandy, the silty, and the biological coast are regarded as research objects, and with data mining technology, found the rules of interpretation of those three types of coastlines. Then, an intelligent coastline interpretation method based on rules was proposed. Firstly, the rules for extracting the waterline in Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper Plus) imagery were discovered. Then, through analyzing the features of sandy, silty and biological coast, the indicators of interpreting different types of shoreline were determined. According to the indicators, the waterline could be corrected to the real coastline. In order to verify the validity of the proposed algorithms, three Landsat TM/ETM imageries were selected for case studies. The experimental results showed that the proposed methods could interpret the coastlines of sandy, silty, and biological coasts with high precision and without human intervention, which exceeded three pixels.
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  • Alesheikh A A, Ghorbanali A, Nouri N. 2007. Coastline change detection using remote sensing. International Journal of Environmental Science and Technology, 4: 61-66
    Chen L C, Rau J Y. 1998. Detection of shoreline changes for tideland areas using multi-temporal satellite images. International Journal of Remote Sensing, 19: 3383-3397
    Cracknell A P. 1999. Remote sensing techniques in estuaries and coastal zones an update. International Journal of Remote Sensing, 20: 485-496
    Dellepiane S, Laurentiis R D, Giordano F. 2004. Coastline extraction from SAR images and a method for the evaluation of the coastline precision. Pattern Recognition Letters, 25: 1461-1470
    Feng Landi, Sun Xiaogong, Xu Kehui. 2002. Edge detection of coastline based on wavelet transform method. Journal of Ocean University of Qingdao, 32(5): 777-788
    Lee J S, Jurkevich I. 1990. Coastline detection and tracing in SAR images. IEEE Transactions on Geoscience and Remote Sensing, 28: 662-668
    Liu H, Jezek K C. 2004. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. International Journal of Remote Sensing, 25: 937-958
    Mason D C, Davenport I J. 1996. Accurate and efficient determination of the shoreline in ERS-1 SAR images. IEEE Transactions on Geoscience and Remote Sensing, 34: 1243-1253
    Niedermeier A, Romaneessen E, Lehner S. 2000. Detection of coastline in SAR images using wavelet methods. IEEE Transactions on Geoscience and Remote Sensing, 38: 2270-2281
    Rasuly A, Naghdifar R, Rasoli M. 2010. Monitoring of Caspian sea coastline changes using object-oriented techniques. Procedia Environmental Science, 2: 416-426
    Ryan T W, Sementilli P J, Yuen P, Hunt B R. 1991. Extraction of shoreline features by neural nets and image processing. Photogrammetric Engineering and Remote Sensing, 57: 947-955
    Tebbens S F, Burroughs S M, Nelson E E. 2002. Wavelet analysis of shoreline change on the Outer Banks of North Carolina: an example of complexity in themarine sciences. Proceedings of the National Academy of Sciences of the United States of America, 99(suppl 1): 2554-2560
    Wang C Y, Zhang J, Ma Y. 2010. Coastline interpretation from multispectral remote sensing images using an association rule algorithm. International Journal of Remote Sensing, 31: 6409-6423
    Winarso G, Judijanto, Budhiman S. 2001. The potential application of remote sensing data for coastal study. In: Proceedings of the 22nd Asian Conference on Remote Sensing. Singapore: CRISP of the National University of Singapore, 87-91
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