.png){kind=icon}
返回
Mapping land use with using Rotation Forest algorithm from UAV images
.png){kind=icon}
OA
AI
DOI：10.1080/22797254.2017.1319252.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The aim of this study is to test the performance of the Rotation Forest (RTF) algorithm in areas that have similar characteristics by using Unmanned Aerial Vehicle (UAV) images for the production of most up-to-date and accurate land use maps. The performance of the RTF algorithm was compared to other ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB). The accuracy assessments showed that the RTF with 84.90% and 93.33% accuracies provided better performance than RF (7% and 4%) and GAB (15% and 11%) in urban and rural areas, respectively. Subsequently, in order to increase the classification accuracy, a majority filter was applied to post-classification images and the overall classification accuracy of the RFT was increased approximately up to 3%. Also, the results of classification were also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the classification of UAV images for land use mapping.
Keyword:
Rotation Forest
Random Forest
Unmanned Aerial Vehicle
classification
McNemar test
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:
3.7
论文数:
921
被引数:
2.2K
机构
引用论文
Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics.png)
APPLIED GEOGRAPHY
APPLIED GEOGRAPHYIF5.4
PREAI
分享收藏分享收藏A statistical significance of differences in classification accuracy of crop types using different classification algorithms使用不同分类算法的作物类型分类精度差异的统计意义
PREAI
分享收藏Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution以250 m分辨率绘制具有时空一致性的全球土地覆盖2001年和2010
PREAI
分享收藏
查看更多内容