基于FSVM时域背景预测的红外弱小目标检测  

Small Target Detection in Infrared Image Sequences Based on FSVM Temporal Background Prediction

作　　者：罗子娟[1] 

机构地区：[1]中国电子科技集团公司第二十八研究所C4ISR技术国防科技重点实验室,南京210007

出　　处：《光电工程》2010年第9期51-57,共7页Opto-Electronic Engineering

摘　　要：本文提出了一种基于模糊支持向量机(FSVM)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,利用模糊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,本文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。A method of small target detection in infrared image sequences is proposed based on the Fuzzy Support Vector Machine （FSVM） temporal background predication. Firstly, the pixel gray value sequences at the same position of the previous frames are fitted with function by using the FSVM optimized. The pixel gray value at the same position of the next frame is predicted by the fitted function. Then, the estimated image subtracted from the source image gives the residual image. The residual image is segmented using two-dimensional Tsallis-Havrda-Charvat＇s entropy thresholding method. The real small target is 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.

关 键 词：红外小目标检测 背景预测 模糊支持向量机 二维Tsallis—Havrda-Charvat熵 

分 类 号：TN215[电子电信—物理电子学]