基于各类各向异性的EM-MAP-HMRF多源遥感图像融合算法

An EM-MAP-HMRF Multi-sensor Image Fusion Algorithm Based on Non-homogeneous Class and Direction

为提高目标识别率,在目标图像融合过程中引入Markov随机场建立类别的先验分布模型,针对模型中参量β的选取问题,提出了基于各类各向异性的期望最大化-最大后验概率-多层次马尔可夫随机场集中式与分布式两种图像融合算法.实验证明,两种融合算法都既可以提高分类准确度,又能够增大抗噪能力,且二者又有不同的特色,可以根据实际要求(如,运算速度、分类准确度、计算负荷等)进行应用选择,用以提高对特定目标进行自动检测与识别的准确性.

In order to improve the target identification,Markov random field（MRF）is introduced in the target fusion process to build prior probability model of a class.Then aiming at selecting model parameter β,an EM-MAP-HMRF feature-level fusion algorithm is proposed based on non-homogeneous class and direction.HMRF is divided into centric and distributed-based fusion schemes.The simulations show that the two new fusion algorithms can improve the classification accuracy,and enhance the ability to anti-interference.However,they have different advantages.The two new schemes can be used in various fusion systems for different applications and improve the effectiveness of detection and identification for specific targets.