Applied research of Gaussian maximum likelihood classification in hyperspectral classification
高斯最大似然分类在高光谱分类中的应用研究

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.