1302 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
. . 
taining characters stable of the whole image and big bright area. 
Whereas Morphological close operation could remove dark de 
tails which is smaller than structure element and maintain char 
acters stable of the whole image and big dark area. Structure 
element with circle shape has good suitability. Taking all above 
factors we design our morphological filter algorithm like fol 
lows: 
Step 1: Employ circular structural element which is bigger 
then the target, training it with background samples, after the 
Morphological opening operation noise and target both will be 
removed. The final result is considered as the background. 
Step 2: The opening operation is acted on the primary image 
with the structural element which was trained by target samples. 
The bright noise whose size is smaller then the structure ele 
ment is filtrated .And the background is consistent with Step 1. 
Thanks to the trained structure elements own target information, 
target will not be filtrated 
Step 3: The result of the image from the step 2 subtracts the 
background images in step 1 is the final target. 
4. MORPHOLOGY ALGORITHM OPTIMIZED BY 
GENETIC THEORY 
The collectivity flow chart of target detection algorithm is 
shown in Figure 1. Figure 2 is a detailed introduction to the 
“Genetic Evolution” part of Figure 1 
Figure 2 Genetic evolution algorithm flow chart 
Figure 1 Optimized morphology target detection algorithm 
by genetic theory 
5. THE APPLICATION OF ALGORITHM 
In order to verify the validity of the algorithm, in this paper, 
two kinds of natural background infrared image were used to 
the experiment. 
a. single objective infrared image b. multi-objective infrared 
image c. salt and pepper noise image 
Figure 3.Original infrared image