.png){kind=icon}
返回
Random Forests for land cover classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2005.08.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy considerably. The most widely used ensemble methods are boosting and bagging. Boosting is based on sample re-weighting but bagging uses bootstrapping. The Random Forest classifier uses bagging, or bootstrap aggregating, to form an ensemble of classification and regression tree (CART)-like classifiers. In addition, it searches only a random subset of the variables for a split at each CART node, in order to minimize the correlation between the classifiers in the ensemble. This method is not sensitive to noise or overtraining, as the resampling is not based on weighting. Furthermore, it is computationally much lighter than methods based on boosting and somewhat lighter than simple bagging. In the paper, the use of the Random Forest classifier for land cover classification is explored. We compare the accuracy of the Random Forest classifier to other better-known ensemble methods on multisource remote sensing and geographic data. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
random forests
classification
decision trees
multisource remote sensing data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
暂无机构信息
引用论文
Photoinduced Electron and Energy Transfer in a Molecular Triad Featuring a Fullerene Redox Mediator
OAAI
.png){kind=icon}
OAAI分享收藏Heavy metals research in Nigeria: a review of studies and prioritization of research needs尼日利亚的重金属研究: 研究回顾和研究需求的优先次序
OAAI
OAAI分享收藏Investigation of the random forest framework for classification of hyperspectral data高光谱数据分类的随机森林框架研究OAAI
OAAI分享收藏分享收藏
查看更多内容