本文在建立各级信息融合模型的基础上，重点讨论了位置级融合和目标识别级融合的算法。
The paper constructed every level information fusion models firstly, and put emphasis on position level fusion and identification level fusion arithmetic.
在目标识别级重点讨论了基于D - S证据理论的目标识别融合，通过性能分析可知该算法具有不需要先验概率和条件概率密度等优点。
In object identification level object identification fusion based on D-S proof theory was discussed, performance analyzing is found that the arithmetic did not need probability distribution.
而图像融合中的特征级融合在目标识别、医疗诊断以及生物特征识别等领域有着越来越重要的作用。
The feature fusion of image has more and more important applications, such as in target recognition, medical treatment and biology feature recognition.
该算法充分利用红外与可见光图像的成像特性，对两个谱段图像的目标识别结果进行决策级的融合，使多谱识别效果大大优于单谱识别。
The algorithm integrates the advantages of infrared image and visible image and makes the recognition result by multiple-spectral information better than single spectrum information.
该算法充分利用红外与可见光图像的成像特性，对两个谱段图像的目标识别结果进行决策级的融合，使多谱识别效果大大优于单谱识别。
The algorithm integrates the advantages of infrared image and visible image and makes the recognition result by multiple-spectral information better than single spectrum information.
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