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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
The static holds two important factors, one is the difference 
between the gray value of current detection kernel and its local 
neighbour pixels, and another is the difference between the 
current detection kernel and the image background. Using the 
two factors, we can detect the so called “anomalies”, whose 
gray values are obviously lower or higher than the image 
background. 
When all the local clusters of anomalies are located, we find all 
the positions of potential targets. According to the image 
coordinate relationship between original images and resample 
images, we can unprotect the detected clusters on the resample 
images to the original images to locate some small image 
blocks, in which one (or maybe a few) potential target should 
be contained each. 
2.3 Recognition 
The output of Location step is a series of small located blocks 
in original image containing one (maybe more than one when 
two or three ships anchor together) potential target each. This 
paper studies on two issues: is there ship in the block and what 
type the ship is. Because there is not a universal automatic 
algorithm for detecting unknown object, the effect of the 
recognition result depends on the given prior knowledge. 
Unsupervised classification (clustering) is a traditional method 
to filter unexpected objects out in multi-spectral data, but it’s 
not perfect for our detection task because sometimes other 
objects show similar spectral characteristic. So a improved 
regional segmentation algorithm based both on the geometry 
features and spectral characteristic of ship objects is 
conducted[Xu Da-qi,2006].In order to recognize the target fast, 
knowledge about the shape and size of ship is required. The 
segmentation algorithm is an iteration of well-known Otsu 
segmentation, with an improvement in enhancing contrast of 
gray value of the image. 
3. EXPERIMENTS 
To validate this method proposed in this paper, an aerial image, 
with lm spatial resolution and RGB three spectral bands, which 
is shot by digital frame camera in Yantai, Shangdong is used. 
The original image size is 2616(width) by 3214(height). On the 
sea surface there are 14 ships, four are navigating, the others are 
anchoring and three ships are anchoring together like one ship. 
The distribution of these ships is as following: 
In searching step, a 3 X 3 kernel is used and the threshold of the 
S value is set to 0.9.The searching results demonstrate the 
efficiency of this method. By using the LISA analysis searching 
method, all the anomalies are detected on the sampled image, 
which performance as red spot in Figure 2.The whole searching 
process on sampled image takes 0.133 second. 
Figure 2.Searching results on the sampled image, whose size is 
130(width) by 160(height). 
A mathematical morphology dilation operation is processed on 
the searching result to make anomalies that belong to the same 
ship to connect to each other. Then each connected anomaly is 
unprojected to the original image and located use red block, 
which is a little larger than the anomalies area, shown in Figure 
3. To avoid false alarm, a constrain is added to the detection 
method that each ship block should have more than 3 anomaly 
spots. So three blocks with only one spot in it are dropped. The 
figure 4 shows the final detection result. All 14 ships are 
detected, but in two blocks, ships anchoring together are 
considered to be one ship by this method. 
Figure 3. The located ships on original image 
Figure 1. The original image.