基于高效置信传播的改进马尔可夫随机场高光谱数据分类算法

An Improved Markov Random Field Classification Approach for Hyperspectral Data Based on Efficient Belief Propagation

针对马尔可夫随机场分类算法中类条件概率估计不准及全局能量最小化计算负担重的问题,提出一种基于高效置信传播的改进马尔可夫随机场高光谱数据分类算法.采用基于光谱信息的统计支持向量机方法提高类条件概率估计精度;通过马尔可夫随机场分类算法引入空间相关信息,实现光谱与空间信息的有效结合;设计一种高效置信传播优化算法,降低计算负担、提高算法精度.实验结果表明该算法平均分类精度达到95.78%,Kappa系数为93.34%,且计算时间约为标准置信传播算法的25%,因此是一种计算负担小、分类精度高且具有实用价值的高光谱数据地物分类方法.

Aiming at the problems of imprecise class conditional probability （CCP） estimation and heavy computational cost for the global energy minimum in Markov random field （MRF） based classification algorithm, an improved MRF approach based on efficient belief propagation （EBP）is developed for land-cover classification of hyperspectral data. The estimation accuracy of the CCP is improved by the probabilistic support vector machine （PSVM） algorithm using spectral information of pixels, then the spatial correlation information is introduced by the MRF classification algorithm, thus the spectral information and spatial information is combined effectively. Moreover, an EBP optimization algorithm is developed, by which the computational cost is reduced and the classification accuracy is improved. The experimental results show that the proposed approach is effective. The average classification accuracy is up to 95.78%, Kappa coefficient is 93.34%, and the computational time of EBP is about 25% of that by belief propagation algorithm. Therefore, the proposed approach is valuable in land-cover classification application for hyperspectral data with low computational cost and high classification accuracy.