CAJ | 학술논문

提出了一种新的嵌入高斯混合模型(GMM,Gaussian Mixture Model)遥感影像朴素贝叶斯网络模型GMM-NBC(GMM based Na(1)ve Bayesian Classifier).针对连续型朴素贝叶斯网络分类器中假设地物服从单一高斯分布的缺点,该方法将地物在特征空间的分布用高斯混合模型来模拟,用改进EM算法自动获取高斯混合模型的参数;高斯混合模型整体作为一个子节点嵌入朴素贝叶斯网络中,将其输出作为节点(特征)的中间类后验概率,在朴素贝叶斯网络的框架下进行融合获得最终的类后验概率.对多光谱和高光谱数据的分类实验结果表明,该方法较传统贝叶斯分类器分类效果要好,且有较强的鲁棒性.
제출료일충신적감입고사혼합모형(GMM,Gaussian Mixture Model)요감영상박소패협사망락모형GMM-NBC(GMM based Na(1)ve Bayesian Classifier).침대련속형박소패협사망락분류기중가설지물복종단일고사분포적결점,해방법장지물재특정공간적분포용고사혼합모형래모의,용개진EM산법자동획취고사혼합모형적삼수;고사혼합모형정체작위일개자절점감입박소패협사망락중,장기수출작위절점(특정)적중간류후험개솔,재박소패협사망락적광가하진행융합획득최종적류후험개솔.대다광보화고광보수거적분류실험결과표명,해방법교전통패협사분류기분류효과요호,차유교강적로봉성.
This paper proposed a new remote sensing images naive Bayesian network model GMM-NBC (GMM based na(1)ve Bayesian classifier) embedded Gaussian mixture model. To aim at the fault of single Gaussian distribution hypothesis in continuous naive Bayesian network classifier, this method simulates the distribution in the feature space using Gaussian mixture model,and gets sub-Gaussian distributions and its parameters of GMM automatically using improved EM algorithm. Gaussian mixture model as a node be embedded into naive Bayesian network, takes the output of Gaussian mixture model as the middle class posterior probability, and obtains the final posterior probability under na(1)ve Bayesian network framework. Experiments of multispectral and hyperspectral images, indicate that the performance of this method is better than traditional Bayesian network classifier, and with strong robustness.