Vegetation extraction method based on color indices from UAV images
DING Leilong1,2, LI Qiangzi2, DU Xin2, TIAN Yichen2, YUAN Chao2
1. School of the Earth Sciences and Resonrces, China University of Geosciences(Beijing), Beijing 100083, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Vegetation extraction plays a key role in such aspects as agricultural monitoring, ecological and environmental function evaluation. Traditionally, it is a labor input task before remotely sensed images are involved. Yet it is difficult to deal with conditions when clouds exist. Therefore, scenes become hopeful when unmanned aerial vehicle(UAV) images emerge in vegetation extraction, and hence this means is a low-cost and flexible way with high spatial resolution. High efficiency methods are therefore required to extract vegetation area automatically using UAV images, preferentially with various color indices always involved. The problem is that there is no evaluation of the effects of vegetation extraction with different color indices. In this paper, Weishan County of Shandong Province was chosen as the study area and four color vegetation indices comprising normalized green-red difference (NGRDI), excess green (ExG), excess green minus excess red (ExG-ExR) and green leaf index (GLI) were selected to extract vegetation information from UAV images with OTSU threshold values. The results show that all the color indices are capable of extracting vegetation area with accuracies above 90%. ExG-ExR index could more likely generate higher accurate results than other indices. ExG and GLI indices generate relatively high accurate and stable results, and could also be used for effective vegetation extraction. For images with high RGB value contrast between vegetation and background, all the color indices work especially well. Further analysis has revealed that accuracies of vegetation extraction have positive relationship with the proportion of winter wheat in images, and exhibit negative relationship with the proportion of broadleaf trees, buildings and roads.
丁雷龙, 李强子, 杜鑫, 田亦陈, 袁超. 基于无人机图像颜色指数的植被识别[J]. 国土资源遥感, 2016, 28(1): 78-86.
DING Leilong, LI Qiangzi, DU Xin, TIAN Yichen, YUAN Chao. Vegetation extraction method based on color indices from UAV images. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 78-86.
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