基于可见光波段无人机遥感的植被信息提取  

Extraction of vegetation information from visible unmanned aerial vehicle images

作　　者：汪小钦[1] 王苗苗[1] 王绍强[2] 吴云东[3] 

机构地区：[1]福州大学空间数据挖掘和信息共享教育部重点实验室、福建省空间信息工程研究中心,福州350002 [2]中国科学院地理科学与资源研究所,北京100101 [3]集美大学理学院,厦门361021

出　　处：《农业工程学报》2015年第5期152-159,159+158,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家科技支撑项目(2013BAC08B01);福建省自然科学基金项目(2012J01166,2013J01158)

摘　　要：无人机遥感具有使用成本低、操作简单、获取影像速度快、地面分辨率高等传统遥感无法比拟的优势。该文通过分析仅含红光、绿光和蓝光3个可见光波段的无人机影像中植被与非植被的光谱特性,同时结合健康绿色植被的光谱特征,借鉴归一化植被指数NDVI的构造原理及形式,提出了一种综合利用红、绿、蓝3个可见光波段的归一化植被指数——可见光波段差异植被指数VDVI(visible-band difference vegetation index)。与其他基于可见光波段的植被指数,如过绿指数EXG(excess green)、归一化绿红差值指数NGRDI(normalized green-red difference index)、归一化绿蓝差值指数NGBDI(normalized green-blue difference index)和红绿比值指数RGRI(red-green ratio index)以及仅用绿光波段的提取结果进行对比分析,结果表明:VDVI植被提取精度高于其他可见光波段植被指数,且阈值在0附近,较易确定。为了验证VDVI的适用性与可靠性,选取与试验影像同一时期拍摄但不同区域的另一影像使用同样的方法提取植被信息。结果表明:VDVI对于仅含可见光波段无人机遥感影像的健康绿色植被信息具有较好的提取效果,提取精度可达90%以上,适用于仅含可见光波段无人机遥感影像的健康绿色植被信息提取。Unmanned Aerial Vehicle （UAV） Remote Sensing has great advantages over traditional methods, such as lower cost, simpler operation, faster access speed and higher resolution. In this paper, after analyzing the spectral characteristics of vegetation and non-vegetation in UAV images, which only contains red, green, and blue bands, we found that the vegetation spectral had the feature of green band＆gt;red band＆gt;blue bands, which means vegetation had the biggest reflection in the green band and had the smallest reflection in the blue band. However, non-vegetation region had the reflection feature of red band＆gt;green band＆gt;blue band and blue band＆gt;green band＆gt;red band. The pixels value of the vegetation region was smaller than the non-vegetation region. For overall consideration of the above characteristics and the features of the healthy green vegetation’s spectral profile, and in order to enhance the vegetation information and minimize the vegetation signal, we referenced the form of NDVI and put forward a new vegetation index--VDVI （visible-band difference vegetation index）. Then we calculated the vegetation index of VDVI, EXG, NGRDI, NGBDI, and RGRI. After calculation of the vegetation index, we used the same AOI region of the prior analysis in a typical spectral characteristic and we made the line charts to analyze the feasibility of each index. After observation of the line charts, we found that NGRDI was not suitable to extract the vegetation from a UAV image because the index values were overlapping with each other, except for bare soil. On the contrary, the NGBDI and VDVI was suitable for the extraction of vegetation from the image, because there was little overlapping. And the vegetation’s EXG index value was greater than twenty and the value of non-vegetation was lesser than twenty except for the area of a building. However, there was also some overlapping of a building and the field, which could cause some mistake in the extraction of the result. After that, we determined th

关 键 词：无人机 植被 提取 可见光波段 可见光波段差异植被指数VDVI 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置] S127[自动化与计算机技术—控制科学与工程]