针对基于线性模型分解高光谱影像混合像元分解精度低, 而非线性模型难以建立等问题, 提出了利用多核支持向量机(MKSVM)的后验概率进行高光谱影像非线性混合像元分解的方法。该方法在支持向量机的基础上, 以线性加权组合核函数代替单核函数, 采用简单多核学习方法迭代解算权系数来实现分类。然后, 通过S型函数将分类器输出值转化为概率; 将两两配对概率转换为多类后验概率。最后, 利用后验概率实现高光谱影像的非线性混合像元分解。采用该方法对两组推帚式超光谱成像仪(PHI)的高光谱影像进行了对比实验, 结果表明: 该方法的分类精度分别提高到95.62%和91.51%, 均方根误差(RMSE)最小分别为11.15%和7.55%, 均小于15%。实验结果显示提出的方法基本消除了混合像元对高光谱影像分类的影响, 提高了分类精度。

As the mixed pixel decomposition based on linear spectrum models has lower decomposition accuracy and the nonlinear spectrum model is difficult to be established, a nonlinear mixed pixel decomposition method for the hyperspectral imagery was proposed based on the posterior probability of Multiple Kernel Support Vector Machine (MKSVM). On the basis of the SVM, the multiple kernel function formed by linear weighted combination was taken to replace the single kernel and the simple multiple kernel learning was used to solve the weights iteratively to achieve the classification. Then, the output values of the classifier were converted to pairwise coupling probabilities by the sigmoid function and then to the multi-class posterior probability. Finally, the hyperspectral imagery decomposition was achieved through the posterior probability. The results from experiments of two push-broom Hyperspectral Imagers (PHIs) show that the classification accuracies of hyperspectral imagery nonlinear mixed pixel decomposition based on MKSVM reach 95.62% and 91.51% ,respectively, the Root Mean Square Errors(RMSEs) are reduced to 11.15% and 7.55%, and both are less than 15%. In conclusion,the influence of mixed pixel on hyperspectral imagery classification is eliminated, and the classification accuracy is increased.