基于多分类器组合的红外目标识别方法

摘要

针对以往的红外目标模式识别方法无法区分坦克与铁板假目标的缺点,提出了基于多分类器组合的红外目标模式识别方法.该方法对红外图像的每行像素使用线性分类器和BP神经网络分类器进行识别,用与规则对两分类器的识别结果进行决策融合,得到每行像素的识别结果,然后对多行像素的识别结果使用多数票规则及或规则进行决策融合,得到最终识别结果,完成对坦克、背景和铁板假目标的区分.仿真结果表明:组合使用BP神经网络分类器和线性分类器,可提高系统识别能力,能较好地完成目标识别.%Aiming at the shortcomings that the traditional infrared target recognition method could not distinguish the tank from fake targets of iron plate, a target recognition method based on classifier combination was proposed. This method used a linear classifier and BP neural network classifier to identify the infrared images by using the AND,OR and Majority rules as the classifier combination methods. Simulation results showed that the combination of the BP neural network classifier and linear classifier could improve the system recognition capability, and promoted the target identification performance.