参数优化随机森林算法的土地覆盖分类

Land cover classification based on algorithm of parameter optimization random forests

针对随机森林算法进行土地覆盖分类时无法确定参数组合以得到最优分类结果的问题,该文提出了两种随机森林算法的参数优化方法。以北京市昌平区为研究区,应用Landsat TM影像,实现了基于光谱值、纹理特征和专题特征的随机森林土地覆盖分类。采用改进网格法和遗传算法对随机森林算法的参数进行选择与优化,比较了改进的网格法和遗传算法方法找到的参数组合最优解,并将优化参数后的随机森林算法与传统的最大似然法及未经参数优化的随机森林算法对比。实验结果验证了随机森林算法在土地覆盖分类上的适用性和稳定性,且该文提出的基于参数优化的随机森林算法能得到更高的分类精度。

According to the fact that in land cover classification there is no suitable method for determining the parameter combination of random forests algorithm to get the optimal classification result, this paper proposed two methods for optimizing the parameters of random forests algorithm. Taking Changping district, Beijing as the study area, this paper employed random forests to perform land cover classification based on spectral, texture and thematic features. Parameters of random forests algorithm were selected and optimized by the improved grid method and genetic algorithm. After comparing the two parameter optimization solutions, this paper compared the land cover classification results by parameters optimization based random forests algorithm with those by traditional maximum likelihood method and random forest without parameter optimization. The experimental results verified the applicability and stability of random forests algorithm on land cover classification and showed that the proposed method has higher classification accuracy.