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高斯最大似然分类在高光谱分类中的应用研究
引用本文:陈进,王润生. 高斯最大似然分类在高光谱分类中的应用研究[J]. 计算机应用, 2006, 26(8): 1876-1878
作者姓名:陈进  王润生
作者单位:国防科技大学ATR国家重点实验室,湖南,长沙,410073;国防科技大学ATR国家重点实验室,湖南,长沙,410073
摘    要:分析了高斯似然分类错误率和Bhattacharyya距离的关系,同时推导出在独立特征条件下Bhattacharyya距离具有相加的性质,并在这些基础上提出了一种新的特征选择算法。该算法以各特征的相对Bhattacharyya和作为准则函数选择能有效降低分类错误率的一组特征,最后利用这组特征进行高斯似然分类。实验采用AVIRIS数据,结果证明了该算法的有效性。

关 键 词:高光谱分类  高斯最大似然分类  分类错误率  Bhattacharyya距离  特征选择
文章编号:1001-9081(2006)08-1876-03
收稿时间:2006-02-16
修稿时间:2006-02-162006-04-13

Applied research of Gaussian maximum likelihood classification in hyperspectral classification
CHEN Jin,WANG Run-sheng. Applied research of Gaussian maximum likelihood classification in hyperspectral classification[J]. Journal of Computer Applications, 2006, 26(8): 1876-1878
Authors:CHEN Jin  WANG Run-sheng
Affiliation:ATR National Key Lab, National University of Defense Technology, Changsha Hunan 410073, China
Abstract:The relationship between Gaussian maximum likelihood classification error and Bhattacharyya distance was analyzed, and the addition property of Bhattacharyya distance was enumerated under uncorrelated features condition. Based on such analyses, a new feature selection algorithm was derived. This algorithm adopted the relative Bhattacharyya distance summation of each feature as the criterion function to select the features which contributed more to the reduction of classification error. These features then could be used for Gaussian maximum likelihood classification. Adopting AVIRIS data, the experimental results verify the effectiveness of this algorithm.
Keywords:hyperspectral classification   Ganssian maximum likelihood classification   classification error   BhattachmTya distance   feature selection
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