摘要
随机森林是一种新兴的、高度灵活的机器学习算法,在预测和分类方面有着良好的稳定性,且算法性能要优于许多单预测器。鉴于此,本文提出了随机森林的遥感影像变化检测算法,利用熵率法对遥感影像进行超像素分割,获取最优分割结果;构建了基于随机森林的遥感影像变化检测模型,以所提取的Gabor特征和光谱特征作为模型输入进行训练和预测,并将有决策树的投票作为最终的变化检测结果。试验结果表明,本文所构建的随机森林变化检测模型在漏检率和虚检率上明显低于其他算法,且总体正确率高,在算法时间上也明显优于其他算法。
Random forest is an emerging and highly flexible machine learning algorithm with good stability in prediction and classification,and the performance of the algorithm is better than many single predictors.In view of this,a remote forest image change detection algorithm for random forests is proposed.The entropy rate method is used to superpixel segmentation of remote sensing images to obtain optimal segmentation results.A remote forest image change detection model based on random forest is constructed.The extracted Gabor features and spectral features are used as model inputs for training and prediction,and the decision tree voting is used as the final change detection result.The experimental results show that the random forest change detection model constructed in this paper is significantly lower than other algorithms in the missed detection rate and false detection rate,and the overall correct rate is the highest,and the algorithm time is also significantly better than other algorithms.
作者
刘霞
郭亚男
LIU Xia;GUO Yanan(School of Computer Science and Engineering,Cangzhou Teachers College,Cangzhou 061001,China;School of Electronic Information Engineering,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《测绘通报》
CSCD
北大核心
2020年第5期16-20,共5页
Bulletin of Surveying and Mapping
基金
国家自然科学基金(51965014)
中国博士后科学基金特别资助(2015T80717)。
