安阳师范学院学报
安暘師範學院學報
안양사범학원학보
JOURNAL OF ANYANG TEACHERS COLLEGE
2012年
2期
34-37
,共4页
EM%混合模型%遥感影像%模糊化策略%分类
EM%混閤模型%遙感影像%模糊化策略%分類
EM%혼합모형%요감영상%모호화책략%분류
Expectation-Maximization%mixture model%Remote-Sensing images%fuzzy strategy%classification
遥感影像的统计分类中,通常都将像点特征的集合视为概率密度函数的混合分布,EM算法是求解这种混合模型参数的一个常用方法。但EM算法在给定合适初值的情况下,对训练数据中的噪声非常敏感,这将严重影响算法的运行效率和求取参数的精度。为了解决这个问题,本文提出了EM算法的模糊化策略,以此来减少噪声在参数学习过程中的影响。对遥感影像的分类实验表明,经过模糊化的EM算法能够更好地完成影像数据的分类。
遙感影像的統計分類中,通常都將像點特徵的集閤視為概率密度函數的混閤分佈,EM算法是求解這種混閤模型參數的一箇常用方法。但EM算法在給定閤適初值的情況下,對訓練數據中的譟聲非常敏感,這將嚴重影響算法的運行效率和求取參數的精度。為瞭解決這箇問題,本文提齣瞭EM算法的模糊化策略,以此來減少譟聲在參數學習過程中的影響。對遙感影像的分類實驗錶明,經過模糊化的EM算法能夠更好地完成影像數據的分類。
요감영상적통계분류중,통상도장상점특정적집합시위개솔밀도함수적혼합분포,EM산법시구해저충혼합모형삼수적일개상용방법。단EM산법재급정합괄초치적정황하,대훈련수거중적조성비상민감,저장엄중영향산법적운행효솔화구취삼수적정도。위료해결저개문제,본문제출료EM산법적모호화책략,이차래감소조성재삼수학습과정중적영향。대요감영상적분류실험표명,경과모호화적EM산법능구경호지완성영상수거적분류。
Among the statistical classification methods of Remote-Sensing images,the distribution of pixel feature sets was always viewed as a mixture of the distributions with different density functions,and the Expectation-Maximization algorithm was one of the most frequently used methods to estimate the parameters of the mixture models.But with appropriate initial parameters,it is very sensitive to classification noises,which would probably slow down the running speed and reduce the accuracy of its results.On this point,a fuzzy strategy of this algorithm was proposed in order to depress the negative influences.The classification experiment on the Remote-Sensing image shows the better characters of our algorithm.
