基于像素分类的自适应SAR图像融合算法  

Adaptive SAR image fusion algorithm based on pixel classification

作　　者：高佳城[1] 林岳松[1] 陈华杰[1] 

机构地区：[1]杭州电子科技大学信息与控制研究所,浙江杭州310037

出　　处：《机电工程》2009年第3期16-19,共4页Journal of Mechanical & Electrical Engineering

基　　金：十一五国防预研基金资助项目(未提供);浙江省教育厅科技计划资助项目(Y200702897)

摘　　要：提高纹理清晰度、保护边缘信息是合成孔径雷达(SAR)图像融合的重要目标。针对该问题,提出了一种基于像素分类的自适应SAR图像融合算法。首先使用canny算子提取图像的边缘并分类,然后利用混合高斯模型和隐马尔可夫树模型对小波系数进行建模;在此基础上使用EM算法求得模型参数,并进一步得到隐状态的概率,也就确定了小波系数的混合高斯分布;接着对两个待融合小波系数不同的类型组合采用不同的融合策略,并以隐状态概率加权;最后通过小波反变换、边缘分类增强获得融合以后的图像。实验结果表明,和传统的融合算法相比,该算法取得了更好的融合效果。Improving texture and preserving edge is the important target of synthetic aperture radar（SAR） image fusion. Aiming at the problem, adaptive SAR image fusion algorithm based on pixel classification was proposed. The edges of two source images were firstly distilled using canny operator, followed by edge classification. Then mixture Gaussian model and hidden Markov tree model were used for image modeling of wavelet coefficients. The model parameters were computed using EM algorithm, which was followed by the computation of hidden states probabilities. After having built the mixture Gaussian models of wavelet coefficients, the fusion rules were selected in terms of the distribution of wavelet coefficients, which was followed by linear compages based on probabilities of hidden states. The final fusion image was obtained by wavelet inverse transform and edge enhancing based on the edge classification. Experimental results show that the algorithm provides significant improvement over conventional image fusion methods.

关 键 词：像素分类 自适应图像融合 混合高斯模型 隐马尔可夫树模型 合成孔径雷达(SAR) 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]