4篇
您的检索式：作者名="FENG Quanlong"
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| 1 | Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data显示文摘Wheat is a major staple food crop in China.Accurate and cost-effective wheat mapping is exceedingly critical for food production management,food security warnings,and food trade policy-making in China.To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping,we present a novel approach that combines a random forest(RF)classifier with multi-sensor and multi-temporal image data.This study aims to(1)determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping,(2)to find out whether the proposed approach can provide improved performance over the traditional classifiers,and(3)examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data.Winter wheat mapping experiments were conducted in Boxing County.The experimental results suggest that the proposed method can achieve good performance,with an overall accuracy of 92.9%and a kappa coefficient(κ)of 0.858.The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%.The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods.We conclude that the proposed approach can provide accurate delineation of winter wheat areas. | Jiantao Liu Quanlong Feng Jianhua Gong Jieping Zhou Jianming Liang Yi Li | 2018 | International Journal of Digital Earth2018，11，8： | 3 |
| 2 | Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier--a case of Yuyao, China 显示文摘 | FENG Quanlong LIU Jiantao | 2015 | Water2015，，7： | 1 |
| 3 | Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier--a case of Yuyao, China 显示文摘 | FENG Quanlong LIU Jiantao | 2015 | Water2015，，7： | 1 |
| 4 | Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm显示文摘This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we mean the extraction of the building footprints with precise position and details.In the first step,vegetation points were extracted using a support vector machine(SVM)classifier based on vegetation indexes calculated from color information,then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings.In the second step,we first determined the building boundary points with a modified convex hull algorithm.Then,we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm.Then,two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints.Eventually,the building edges were regularized to form the final building footprints.Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images. | Yucheng Dai Jianhua Gong Yi Lia Quanlong Feng | 2017 | International Journal of Digital Earth2017，10，11： | 0 |
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