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基于最小二乘支持向量机时域背景预测的红外弱小目标检测

期刊名称：兵工学报
时间：0
页码：678-684
语言：中文
分类：TN911.73[电子电信—通信与信息系统;电子电信—信息与通信工程] TP391.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]南京航空航天大学信息科学与技术学院,江苏南京210016
相关基金：国家自然科学基金资助项目（60872065）
相关项目：基于Contourlet变换和混沌粒子群的红外小目标检测方法

关键词：信息处理技术, 红外小目标检测, 背景预测, 最小二乘支持向量机, 粒子群优化, 二维Tsallis-Havrda-Charvat熵, information processing technique, small infrared target detection, background prediction, least squares support vector machine, particle swarm optimization, two-dimensional Tsallis-Havrda-Charvat entropy

中文摘要：

针对信噪比较低时,如何有效地抑制自然背景对目标检测的影响,提出了一种基于最小二乘支持向量机（LS-SVM）时域背景预测的红外弱小目标检测方法。首先针对前几帧图像中对应同一位置像素点的灰度值序列,利用参数经粒子群优化的最小二乘支持向量机进行函数拟合,并据此预测下一帧图像在该位置处像素点的灰度值;然后将原始图像与预测图像相减得到预测残差图像,利用基于二维Tsallis-Havrda-Charvat熵的阈值选取快速算法进行分割,并根据小目标运动的连续性和轨迹的一致性进一步分离噪声和小目标。文中给出了实验结果及分析,并与现有的检测红外小目标的空域和时域背景预测算法进行了比较。结果表明所提出的算法具有更高的检测概率,明显优于已有的基于背景预测的红外小目标检测算法。

英文摘要：

Aiming at suppressing the influence of natural background on the target detection effectively in lower signal-to-noise ratio, a method of small target detection in infrared image sequence was proposed based on the least squares support vector machine （LS-SVM） temporal background predication. Firstly, the gray value sequences at the same pixel locations in the previous frames were fitted by using the LSSVM optimized by the particle swarm. The gray value at the same pixel location in the next frame could be predicted by the fitted function. Then, the estimated image subtracted from the source image gave the residual image. The residual image was segmented by using two-dimensional Tsallis-Havrda-Charvat＇s entropy threshold method. The real small target was confirmed by the continuity and consistency of its movement. The experimental results were given and analyzed. They were compared with those of the existing method of spatial or temporal background predication. The results show that the proposed method can precisely detect the small infrared target and it is superior to the existing method.

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