基于面向对象随机森林方法的滨海湿地植被分类研究

基于Sentinel-2数据,以盐城国家级珍禽自然保护区核心区为研究区,采用基于面向对象的随机森林模型对研究区内的湿地信息进行分类研究. 首先,对影像进行分割处理,计算光谱特征、纹理特征、水体指数、植被指数与纹理特征,并对特征重要性进行排序筛选. 其次,基于此构建5种特征组合方案,并对研究区进行分类,比较不同组合的分类精度找出研究区最优的特征组合方案. 最后,实验表明:通过特征优选后的随机森林算法进行分类效果最好,总体精度达到87.07%,Kappa系数为0.84. 其中互花米草在3种植被中分类精度最高,为97.73%. 证明此方法能够有效提高滨海湿地的分类精度,可用作该区域湿地变化研究.

A Study of Coastal Wetland Vegetation ClassificationBased on Object-oriented Random Forest Method

Based on Sentinel-2 data,this paper uses the core area of Yancheng National Rare Bird Nature Reserve as the study area,and uses an object-oriented random forest model to classify the wetland information in the study area. Firstly,the images are segmented,based on which the spectral features,texture features,water body index,vegetation index and texture features are calculated and ranked; based on this,five feature combination schemes are constructed and the study area is classified,and the best feature combination scheme is found by comparing the classification accuracy of different combinations. The results show that the random forest algorithm after feature selection is the best,with an overall accuracy of 87.07% and a Kappa coefficient of 0.84. Among the three vegetation species,the classification accuracy of Spartina Alterniflora is the highest at 97.73%,which proves that this method can effectively improve the classification accuracy of coastal wetlands and thus can be used for the study of wetland change in the region.