摘要
目的无人机遥感具有高时效、高分辨率、低成本、操作简单等优势。但由于无人机通常只携带可见光传感器,无法计算由可见光—近红外波段组合所构造的植被指数。为解决这一问题,提出一种归一化色调亮度植被指数NHLVI(normalized hue and lightness vegetation index)。方法通过分析HSL(hue-saturation-lightness)彩色空间模型,构建一种基于色调亮度的植被指数,将该植被指数以及其他常用的可见光植被指数,如归一化绿红差值指数NGRDI(normalized green-red difference index)、过绿指数Ex G(excess green)、超绿超红差分指数Ex GR(excess green minus excess red)等,分别与野外实测光谱数据和无人机多光谱数据的NDVI(normalized difference vegetation index)进行相关性比较;利用受试者工作特征曲线ROC(receiver operating characteristic curve)的特点确定阈值,并进行植被信息提取与分析。结果 NHLVI与NDVI相关性高(R2=0.776 8),而其他可见光植被指数中,NGRDI与NDVI相关性较高(R2=0.687 4);ROC曲线下面积大小作为评价不同植被指数区分植被与非植被的指标,NHLVI指数在ROC曲线下面积为0.777,小于NDVI(0.815),但大于NGRDI(0.681),区分植被与非植被能力较强。为进一步验证其精度,利用阈值法提取植被,NHLVI提取植被信息的总体精度为82.25%,高于NGRDI(79.75%),尤其在植被稀疏区,NHLVI的提取结果优于NGRDI。结论提出的归一化色调亮度植被指数,提取植被精度较高,适用于无人机可见光影像植被信息提取,为无人机可见光影像的应用提供了新方法。
Objective An unmanned aerial vehicle （UAV） exhibits many advantages in remote sensing because of its high efficiency,high resolution,low cost,and simple operation.However,most UAVs carry only visible-light true-color sensors,which only contain red,green,and blue bands.Generating some of the most commonly used visible-near infrared-based vegetation indices,such as normalized difference vegetation index （NDVI） and soil-adjusted vegetation index,is difficult.Although hyperspectral and multispectral sensors can produce the previously mentioned indices,the high cost and complexity of data acquisition hinder the further development of UAV technology in the field of vegetation remote sensing.A new vegetation index,which can fully utilize visible-light true-color image in the HSL color space,called Normalized Hue and Lightness Vegetation Index （NHLVI）,has been proposed to solve this problem.Method The characteristics of hue and lightness of different objects in the HSL color space model were analyzed within visible-light true-color image,and the hue （H） and lightness （L） components were selected because of their weak correlations.After their normalization,NHLVI was constructed on the basis of the form of NDVI to enhance the vegetation information.A total of 88 visible true-color and 163 multispectral images,which covered a test area with different vegetation types and coverage,were acquired through a UAV flight campaign to verify the validity of this new vegetation index.The structure from motion algorithm was used to mosaic the UAV images,and the digital orthophoto map of the test area was produced.Then,the NHLVI and several commonly used visible vegetation indices,i.e.,normalized green-red difference index （NGRDI）,excess green,vegetation index,color index of vegetation,excess green minus excess red,combination,and combination 2,were calculated.Hyperspectral datasets were simultaneously collected using the ASD HandHeld2 during the UAV flight and were resampled to match visible true-color and mult
出处
《中国图象图形学报》
CSCD
北大核心
2017年第11期1602-1610,共9页
Journal of Image and Graphics
关键词
HSL变换
植被指数
无人机
可见光
植被提取
hue-saturation-lightness （HSL） transform
vegetation index
unmanned aerial vehicle （ UAV ）
visible- light
vegetation extraction
