Figure 4. Defining threshold value for land and sea part of the 
images using 100x 100 search window. 
As seen from the histogram, in the area consisting of 10000 
pixels, the gray value of 4477 pixels was 105, the gray value of 
5461 pixels was 0, and the gray values of 162 pixels was 141 
(Figure 5). Hence gray value of noise was assigned as 105. If 
the number of noise pixel was % 30 graters then the number of 
0 pixels then the image is accepted as noisy. If the image was 
too noisy, than the gray values of noisy pixels were removed. If 
the image was less noisy than next step was processed. 
  
Figure 5. A Histogram consisting of 100x100 pixels from the 
sea part of the image. 
After the elimination of pixels having regular noise value in the 
image scene, a mathematical morphology was used to eliminate 
pixels having random noise value (Acar, U., 2011). In the water 
part of the image, in order to remove the gray values of noise 
pixels by the dominant O (zero) pixel values a 3x3 circle 
structuring element was used. The reason preferring the 3x3 
circle structuring element is that the area of noise pixels was not 
greater than 1 pixel, and mathematical morphology applied with 
this structuring element was sufficient to eliminate the noise 
(Figure 6). 
  
Figure 6. Mathematical morphology opening applied image. 
The applied image processing techniques caused some gaps and 
distortions on coastline. In order to reduce this gap and 
corruption, again a mathematical morphology was applied as 
closing this time. In this process a 5x5 circle structuring element 
was preferred. The reason to prefer the 5x5 circle Structuring 
elements is its ability to eliminate distortion and gaps up to 5 
pixels. The larger circle structuring selection results in the loss 
of the small bays and recesses (Figure 7). 
  
Figure 7. Mathematical morphology closing applied image 
After mathematical morphology application, despite the 
possibility of remaining noise in the image, one more filter was 
applied. Task of this filter was to eliminate gray value of noise 
in a group of pixels when there were only zero gray values and 
noise gray values in that group of pixels (Figure 8). 
  
Figure 8. Eliminating remaining noise gray values. 
For the resulting image a fit-coast algorithm (Bayram, B., 2008) 
was used (Figure 9). It is a region growing algorithm using 
image processing techniques. 
  
  
  
Figure 9. The result of Fit-Coast algorithm application 
Sobel operator was applied on the binary image generated by 
fit-coast algorithm and then image was converted to vector data 
(Figure 10).