MORPHOLOGY INFRARED IMAGE TARGET DETECTION ALGORITHM OPTIMIZED 
BY GENETIC THEORY 
a State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 
University, P.R. China, -shaozhenfeng - 163.com, xianqiangzhu@163.com 
b School of Remote Sensing and Information Engineering, -rsliujun@mail.whu.edu.cn 
KEY WORDS: Genetic Algorithm, Morphology Filter, Optimization Multi-Group Evolution, Target Detection, Infrared Image 
ABSTRACT: 
This paper proposes a novel morphology algorithm for target detection of the infrared images, which is optimized by the genetic al 
gorithm. First, several improvements have been adopted for genetic algorithm (GA). The improvements include the auto-regulation 
of genetic evolution crossover probability and mutation probability which based on population difference; A more reasonable target 
characteristic variable according the feature of infrared image has been designed to train structure elements. So the efficiency of the 
algorithm can be improved obviously, at same time, the optimized trained structural elements could reflect the sample’s true structure 
information. Then the background and objectives structural elements could be got by inputting background and target samples into 
the GA model. Using it as a priori knowledge of the morphology operation, it does favor to improve the algorithm’s accuracy and 
adaptability. .Experiment shows that this method can achieve a higher detection efficiency and accuracy. 
1. INTRODUCTION 
Infrared has been widely used in the target detection technology 
for its special recognition capability and the anti-jamming 
ability. However, due to the detection range of the infrared 
detectors and other conditions, infrared images are 
characterized by severe noise pollution and low signal-to-noise 
ratio. Therefore there is a growing interest in the research of the 
methods which are used in the target detection of the infrared 
images. In recent years, mathematical morphology which based 
on shape and structure information has been paid more and 
more attention in the area of target detection for its superior 
performance in the field of infrared target detection, however, 
the results of the target detection are due to the choice of the 
structural information mentioned above. 
The idea of the genetic algorithms (GAs) which derived from 
the theory of evolution is different from the traditional 
optimization methods. GAs can be used to solve complex 
unstructured problems for its global optimal search capability 
and parallel computing characteristic. Using GAs, we can get 
the optimal structure elements which contain samples’ 
information by training the Genetic Evolution Model (GEM) 
with only a small sample of the origin images. This paper 
demonstrates how multi-populations GA can be employed in 
the target detection using improved mathematical morphology. 
IMPROVED GENETIC ALGORITHM 
GA has particular advantages in solving the problem of multi 
parameter optimization for its global search mechanism, but on 
contrary to traditional GA there are also some shortcomings that 
can not be ignored, such as the powerful global search capabil 
ity while poor local search ability , premature problem, the con 
tradictions between the search efficiency and accuracy. Aiming 
at improving these weaknesses, many scholars have done a lot 
of work to improve its performance, such as the introduction of 
population difference, improved genetic operators and so on. In 
this paper, the genetic algorithms is used into the field of target 
detection and a new fitness measurement methods based on tar 
get characteristics value is proposed for the specific needs of 
detection. For the premature problem, basing on the population 
difference which is proposed by predecessors, the introduction 
of the male and female parents dynamic clustering based on in 
dividual differences makes crossover operate more reasonable; 
The strategy of multi-population parallel evolution and best in 
dividual transplant merge the best individual retention strategy 
and the best gene retention strategy effectively, which acceler 
ate the speed of convergence of the algorithm and meanwhile 
ensure the global search capability. 
Population Difference 
From the perspective natural evolution, if the group owns a 
great diversity larger crossover and smaller mutation ratio is the 
best choice. On the contrary, smaller cross scale and larger mu 
tation probability is better choice, so we can see population dif 
ference is a useful variable in determining evolution parameters. 
Details refer to the references [1]. 
2.2 Encoding Method 
In this paper, we have chosen a real-parameter coding 
method which avoids information loss during the encoding and 
decoding process. Compared to binary coding method, every 
individual in the population contains original information of the 
structure elements. In the abstract this coding method has a 
more appropriate complexity and higher accuracy result. 
Group Initialization