改进特征空间的红外弱小目标背景建模法

Infrared dim small target background modeling based on improved eigenspace mode

摘要:

为有效去除动态背景对弱小目标信号的干扰，提出改进特征空间的红外弱小目标背景建模法来抑制背景。先采用改进的各向异性滤波算法从空域角度进行滤波以约束图像各个组分的差异，紧接着取连续时间域上多帧滤波后的图像组成一个特征矩阵，借助于主成分分析法进行特征分解，最后将输入图像投影到特征空间上进行背景建模，同时为了适应动态变化的背景，在时域上以一定学习率来更新背景模型。实验结果表明，提出的算法比传统的算法取得更好的背景估计效果，结构相似性SSIM、对比度增益I和背景抑制因子BIF分别大于0.97、15.46和5.25。

Abstract:

A background modeling method of infrared dim small target based on improved eigenspace is proposed in order to effectively remove the interference of dynamic background on dim small target signal. Firstly, an improved anisotropic filtering algorithm is employed to filter from the spatial perspective to constrain the differences of each component of the image. Then, a feature matrix is formed from the filtered images in the continuous time domain, and the Principal Component Analysis(PCA) is adopted to perform feature decomposition. Finally, the input image is projected onto the eigenspace for background modeling. As to adapt to the dynamic background, the background model is updated with a certain learning rate in temporal domain. Experimental results show that the proposed algorithm achieves better background estimation effect than the traditional algorithm. The structural similarity SSIM, contrast gain I and background suppression factor B_IF are greater than 0.97, 15.46 and 5.25 respectively.