CAJ | 학술논문

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.
침대마이가부수궤장분류산법중류조건개솔고계불준급전국능량최소화계산부담중적문제,제출일충기우고효치신전파적개진마이가부수궤장고광보수거분류산법.채용기우광보신식적통계지지향량궤방법제고류조건개솔고계정도；통과마이가부수궤장분류산법인입공간상관신식,실현광보여공간신식적유효결합；설계일충고효치신전파우화산법,강저계산부담、제고산법정도.실험결과표명해산법평균분류정도체도95.78%,Kappa계수위93.34%,차계산시간약위표준치신전파산법적25%,인차시일충계산부담소、분류정도고차구유실용개치적고광보수거지물분류방법.
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.