International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
can be modified, therefore they depend on a series of factors, 
such as: state of the sea, oil presence in the water, position of 
the ship in relation to the aimed of the satellite, incidence angle 
of the image, amongst others. Besides these characteristics, in 
the case of ships in movement, the probable targets can also be 
identified for the existence of its wake, that is to say, the 
superficial waves caused by its movement. 
In this work we will show the result of the use of the techniques 
above metioned maids with the purpose of ships detection. For 
this we will use the software described by Rocha et al (2001) 
and Rocha and Stech (2003). In this software routines based on 
the methodologies above cited had been implemented, being 
accomplished studies regarding the values of the statistical 
parameters of each distribution. After the observation of the 
results obtained in diverse tests using diverse values for such 
parameters, it was decided to use fixed values for each 
statistical parameter in accordance with each distribution. Thus 
each distribution had its statistical parameters established in 
order to provide the best adaptation of the curve of its 
histogram. In the case of the methodology described by 
Eldhuset (1996) it was made only a change in the value used as 
factor of comparison by this author. 
After the act of receiving of the image, your digital processing 
was initiated using Geomatica software. The image was, 
initially, analyzed visually. After that visual analysis the image 
was converted of 16 bits for 8 bits. Soon after the image was 
registered using the ephemrides data that follow the image. 
Made this, the image was exported to the “raw” format so that it 
could be processed in software in question. Then, we made a 
subset of the image of 2500 for 2500 pixels with the purpose to 
reduce the computational effort. 
In this software, the image was processed in order, in a first 
stage, to discover which the value of grey level corresponds to a 
probability of 99,5% of pixel to be a target. Discovered this 
value of threshold, the value of the grey level of each pixel of 
the image is compared with it and, in case that it is superior, it 
will be associated in the resultant image to one pixel with value 
255, that is to say, white. Otherwise it will be associated with 
one pixel with value 0, generating black pixels in the exit 
image. This way, the generated image will show pixels with a 
minimum probability of 99,595 to be a target with the white 
color and pixels with probability lower than 99,5% to be a 
target with the black color. Only in the case of the use of the 
methodology described by Eldhuset (1996) this procedure is not 
obeyed, therefore in this case we worked with a 2x2 mask 
covering the original image and generating an intermediary 
image, that is compared with a factor of comparison described 
by the author. 
3. RESULTS AND DISCUSSIONS 
Remote Sensing is a tool wide used at the present time for 
diverse activities. In the case of ship detection, amongst other 
areas, the use of SAR images has been highly used with great 
efficiency. The fact of it could be acquired independent of the 
clouds covering or the conditions of illumination, that is, as 
much of day as the night, is a great big shot of these images. 
The existence of speckle can make it difficult, but it does not 
make impracticable the use of these images for this application. 
The noise speckle is inherent to the process of formation of 
SAR images, not being able therefore to be discarded. 
However, the use of statistical distributions comes being used 
with the purpose to allowing the digital processing of SAR 
images in order to minimize the influence of speckle, without 
with this causing the loss of information. In this work we can 
126 
verify the mentioned sttement. In Figure | we can see the 
original image. This image was processed in the software 
described by Rocha et al (2001) and Rocha and Stech (2003) 
and, as we can see in Figures 2 to 7, the diverse tested 
methodologies presented excellent results, only occurring small 
inherent. variations to the characteristics of the used statistical 
distributions. 
   
   
  
n 
Figure 1. Original image 
The image generated by K distribution, shown in Figure 2, went 
to that presented a bigger amount of noise in the oceanic area 
and a bigger answer in the land area. 
x Ud n = 
Figure 2. K distribution processed image 
These noises are represented in the image as diverse white 
points in the oceanic area, could cause conflict in the 
interpretation, because they could be interpreted as false targets. 
Such fact happens by virtue of threshold established by 
software for this distribution, as cited previously, to have been 
established in a value not very high. 
As we can see in Figures 3 and 4, the images generated by the 
processing based on the use of the distributions Rayleigh and 
Weibull, respectively, presented very good results, without the 
presence of noises in the oceanic area, as it occurred with 
distribution K. The image generated for the Weibull distribution 
still presented a good reply in the land area. 
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