基于无人机影像的可见光波段植被信息识别  

Vegetation information recognition in visible band based on UAV images

作　　者：高永刚[1,2,3] 林悦欢 温小乐 简文彬[1,2] 龚应双[3] Gao Yonggang;Lin Yuehuan;Wen Xiaole;Jian Wenbin;Gong Yingshuang(College of Environment and Resources,Fuzhou University,Fuzhou 350116,China;Fujian Provincial Universities Engineering Research Center of Geological Engineering,Fuzhou 350116,China;The Academy of Digital China(Fujian),Fuzhou 350108,China)

机构地区：[1]福州大学环境与资源学院,福州350116 [2]地质工程福建省高校工程研究中心,福州350116 [3]数字中国研究院(福建),福州350108

出　　处：《农业工程学报》2020年第3期178-189,共12页Transactions of the Chinese Society of Agricultural Engineering

基　　金：福建省自然科学基金(2019J01649);国家自然科学基金(41861134011、41801393)。

摘　　要：该文通过对6种典型地物在无人机影像可见光波段的光谱特性分析,提出一种基于红、绿、蓝波段的可见光植被指数—超绿红蓝差分指数EGRBDI(excess green-red-blue difference index),并运用该植被指数与18种基于可见光波段的植被指数进行精度比较研究。研究表明,在利用均值和1倍标准差获得的区间范围内,EGRBDI各地类之间的信息无重叠交叉现象;该指数能对植被覆盖相对稀疏区域进行植被信息识别,其总体精度为97.67%,Kappa系数为0.9415,较其他18种指数具有更好的植被信息识别能力。利用不同地物覆盖情况的3幅无人机影像作为数据源,对EGRBDI适用性和稳定性进行研究,结果表明,在3个研究区中,基于EGRBDI的植被信息识别总精度均高于93%,Kappa系数均大于0.85,提取精度受地物类型差异影响的波动性较小,能较好地削弱影像中阴影等因素的影响,具有较好的适用性、可靠性和提取精度。Nowadays,UAV(unmanned aerial vehicle)remote sensing has been widely used in various research fields,due to its incomparable advantages over traditional satellite remote sensing,such as lower cost,fast image access,and high spatial resolution,and so on.But most of the vegetation indices are constructed based on visible bands and near-infrared bands of satellite remote sensing images,and few of them are constructed only based on visible-light bands.Thus,it is necessary to construct a universal vegetation index that is suitable for the visible-light bands of UAV images.According to the analysis of the spectral characteristics of 6 kinds of typical features based on regions of interest in visible-light images from UAV images,this paper proposed a new vegetation index based on red,green and blue bands,named Excess green-red-blue difference index(EGRBDI).The formula of EGRBDI was that the sum value between the square of 2 times green band and the product of red and blue bands divided the difference value of them.The value range of EGRBDI was the interval[-1,1].To determine the accuracy and reliability of EGRBDI,18 kinds of vegetation indices had been studied in this paper,such as CIVE GLI,ExG,and so on.The overlap between different object types was obtained by calculating the mean value and 1-fold standard deviation of vegetation indices.The results showed that EGRBDI,GLI,ExG,g,CIVE,RGBVI,and V-MSAVI had no overlap between vegetation and non-vegetation information,while other vegetation indices appeared the different degree of overlap.Moreover,EGRBDI effectively enlarged the identification range of vegetation information and reduced the identification range of non-vegetation information.When the grey histogram of vegetation index existed distinct bimodal peaks,the corresponding discrimination performance of ground features was relatively strong.Therefore,the quantized interval of gray histograms should be normalized to the interval[0,255]for the comparative analysis between the indices.Results of the analysis concluded

关 键 词：遥感 植被 光谱分析 无人机 可见光波段 超绿红蓝差分指数 

分 类 号：P237.3[天文地球—摄影测量与遥感] TP751.1[天文地球—测绘科学与技术]