Full text: Technical Commission III (B3)

    
ques caused some gaps and 
to reduce this gap and 
orphology was applied as 
5 circle structuring element 
the 5x5 circle Structuring 
istortion and gaps up to 5 
selection results in the loss 
27). 
  
y closing applied image 
application, despite the 
image, one more filter was 
minate gray value of noise 
only zero gray values and 
els (Figure 8). 
  
g noise gray values. 
orithm (Bayram, B., 2008) 
growing algorithm using 
  
  
algorithm application 
inary image generated by 
s converted to vector data 
    
  
  
  
  
  
  
FigurelO. Automated extorted coastline converted to vector 
data. 
5. RESULTS 
Since coastal management requires rapid, up-to-date, and 
correct information, coastal movements have primary 
importance for coastal managers. Remote sensing is an 
important technique for detecting and monitoring coastlines 
using satellite images. In the literature most of the algorithms 
developed for optical images have been discussed in detail. In 
this study the use of SAR images was investigated for automatic 
coastline detection by using PALSAR images. 
The algorithm was applied on 4 images gathered in 2007 and 
2010 in two polarizations as HH and HV. In order to test the 
accuracy of coastline detection, all images were digitized 
manually. To calculate the total difference between the 
automatic coastline detection and manually digitizing, land side 
of the each image was converted to a closed polygon and its 
area was calculated. Then the calculated areas of manual and 
automatic extractions were compared Table 2. Since digitizing 
coastline from PALSAR images is not an easy and simple task 
due to the feature of SAR images, for each pair the manual 
digitizing which was thought to be the best coastline extraction 
was chosen. 
  
  
  
  
  
Automatic Manual 
Image Digitalized(pixel) | Digitalized (pixel) 
2007 HV 315504 
2007 HH 315804 pes 
2010 HV 316805 
2010 HH 316887 316360 
  
  
  
  
  
Table 2. Comparison between automatic coastline detection and 
manual digitizing. 
Overall length of the coastline extracted automatically was 
compered with the manually digitized coastline (Table 3).For 
each image scene the difference is less than %0.9. 
  
  
  
  
  
  
  
  
  
  
Manual Automatic Automatic 
Image Digitalized | Digitalized Digitalized 
8 (pixel) (pixel) (Percent) 
2007 HV 24716 0,61 
2007 HH 24506 24355 0,85 
2010 HV 24898 0,41 
omy om 25055 021 
  
  
Table 3. Comparison between the lengths of automatically 
extracted and manually digitized coastlines. 
6. REFERENCES 
Acar, U., 2011 Data Extraction With Similar Techniques: 
Satellite Images And Medical Phd. Thesis (In Turkish). YTU 
Istanbul Turkey. 
Bayram, B., Acar, U., Seker, D., Ari, A., 2008. A novel 
algorithm for coastline fitting through a case study over the 
Bosphorus. Journal of Coastal Research, 24(4), 983-991. 
Chen, K.S., Wang, H.W., Wang, C.T., Chang, W.Y., 2011, A 
Study of Decadal Coastal Changes on Western Taiwan Using a 
Time Series of ERS Satellite SAR Images, IEEE Journal of 
Selected Topics in Applied Earth Observations and Remote 
Sensing, 4(4), 826-835 
Liu, H. and 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(5), 937—958. 
Niedermeier, A., Romaneessen, E. and Lehner, S., 2000, 
Detection of coastlines in SAR images using wavelet methods. 
IEEE Transactions on Geoscience and Remote Sensing, 38, pp. 
2270-2281. 
Karsli, F., Gunerioglu, A., Dihkan., M., 2011, Spatio temporal 
shoreline changes along the Black Sea coastal zone, Journal of 
Applied Remote Sensing, Vol-5, 2011. 
Wang, Y., Allen, R. T., 2008, Estuarine shoreline change 
detection using Japanese ALOS PALSAR HH and JERS-1 L- 
HH SAR data in the Albemarle-Pamlico Sounds, North 
Carolina, USA, International Journal of Remote Sensing, 
29(15), 4429-4442. 
Ouyang, Y., Chong, J., Wu, Y., 2010, Two coastline detection 
methods in Synthetic Aperture Radar imagery based on Level 
Set Algorithm, International Journal of Remote Sensing, 31 (17- 
18), 4957-4968. 
Wang, C., Zhang, J., Ma, Y., 2010, Coastline interpretation 
from multispectral remote sensing images using an association 
rule algorithm, International Journal of Remote Sensing, 31 (24), 
6409-6423 
Yu, K., Hu, C., Muller-Karger, E.F., Lu, D., Soto, I, 2011, 
Shoreline changes in west-central Florida between 1987 and 
2008 from Landsat observations, International Journal of 
Remote Sensing, 32 (23), 8299-8313 
 
	        
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