结合随机森林面向对象的森林资源分类

针对森林资源分类研究较少且缺少相对简单有效的方法的情况,提出一种结合面向对象和随机森林的森林资源分类方法。面向对象分割技术可减少“椒盐效应”,随机森林分类算法具有高准确度、抗噪声能力强、性能稳定等优势。鉴于此,通过调整面向对象的分割参数,构造最优特征空间及估算随机森林中决策树的数量等,构建了最优的面向对象随机森林分类模型。另外,选择了SVM算法作对比试验。试验结果显示,本文分类算法的总体精度达到83.34%,Kappa系数为0.7892,明显高于SVM,能够有效提高森林资源分类的精度。

Forest resource classification based on random forest and object oriented method

Given that there are few studies on forest resource classification with the lack of relatively simple and effective methods, a forest resource classification method integrating object-oriented segmentation and random forest is proposed in this paper. Object-oriented segmentation technology could efficiently reduce the "salt and pepper effect", and random forest classification algorithm has the advantages of high accuracy, strong anti-noise ability and satisfying stability. Therefore, we built the optimum random forest classification model by adjusting the object-oriented segmentation parameters, constructing the optimal feature space and estimating the number of decision trees in random forests. Besides, the SVM algorithm is taken into comparison. The results show that the overall accuracy of the classification algorithm in this study is 83.34%with the Kappa coefficient reaching 0.789 2, which are significantly higher than that of SVM algorithm. It proves that object-oriented random forest classification can effectively improve the accuracy of forest resource classification.