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作物学报 ›› 2020, Vol. 46 ›› Issue (7): 1112-1119.doi: 10.3724/SP.J.1006.2020.91066

• 耕作栽培·生理生化 • 上一篇    下一篇

无人机影像拼接软件在农业中应用的比较研究

陈鹏飞1,2,*(),徐新刚3   

  1. 1 中国科学院地理科学与资源研究所 / 资源与环境信息系统国家重点实验室, 北京100101
    2 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023
    3 国家农业信息化工程技术研究中心, 北京100097
  • 收稿日期:2019-11-06 接受日期:2020-01-15 出版日期:2020-07-12 网络出版日期:2020-01-28
  • 通讯作者: 陈鹏飞
  • 基金资助:
    国家自然科学基金项目(41871344);国家重点研发计划项目(2017YFD02015);国家重点研发计划项目(2017YFD0201501-05)

A comparison of photogrammetric software packages for mosaicking unmanned aerial vehicle (UAV) images in agricultural application

CHEN Peng-Fei1,2,*(),XU Xin-Gang3   

  1. 1 State Key Laboratory of Resources and Environment Information System / Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
    3 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
  • Received:2019-11-06 Accepted:2020-01-15 Published:2020-07-12 Published online:2020-01-28
  • Contact: Peng-Fei CHEN
  • Supported by:
    National Natural Science Foundation of China(41871344);National Key Research and Development Program of China(2017YFD02015);National Key Research and Development Program of China(2017YFD0201501-05)

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摘要：

无人机遥感为精准农业管理提供了新的工具。实现无人机影像高精度自动拼接是开展无人机遥感应用的基础。目前, 已有不同无人机影像拼接软件在几何精度方面性能比较的研究, 但关于光谱精度方面还未有相关研究, 而其对定量遥感反演非常重要。本研究比较了目前最受欢迎的两款无人机拼接软件Pix4D和Photoscan在农业应用时, 拼接影像过程对原始影像光谱信息的影响, 以为用户推荐最优软件。为此, 基于冬小麦氮肥梯度试验, 本研究在小麦生长季利用无人机搭载多光谱传感器获取相关影像, 并将其分别基于Pix4D软件, Photoscan软件拼接处理。同时, 基于传感器厂商提供的单张影像处理技术, 将每次传感器拍摄数据处理成未拼接的单张多光谱影像。基于以上数据, 在施肥处理小区随机布设样点, 分别提取3种类型影像上的样点光谱信息, 比较它们光谱反射率及其对比度的差异。结果表明, 尽管Pix4D拼接影像和Photoscan拼接影像各波段光谱反射率都与单张影像的反射率有显著相关性, 但与Pix4D拼接影像相比, Photoscan拼接影像的光谱反射率和变异系数与原始单张影像之间更为接近。Photoscan能保留更多的原始光谱信息。结合已有关于两款软件在几何精度和价格方面的比较研究, 本研究推荐Photoscan为农业应用时的最优性价比软件。

关键词: 无人机, 正射拼接, Photoscan, Pix4D, 农业

Abstract:

Unmanned aerial vehicle (UAV) remote sensing technology provides a new tool for precise agricultural management. High precision automatic stitching of UAV images is the basis for application of UAV remote sensing technology. At present, there exist researches to compare the performance of stitching software packages on their spatial precision, but few researches on the evaluation of their spectral precision, which is very important for quantitative remote sensing inversion. The objective of this study was to compare the influence of the two most popular UAVs orthomosaic-processing software packages, Pix4D and Photoscan, on the spectral information of original single image when stitching the image in agricultural application and to recommend the better one for users. For this purpose, multispectral sensor was carried on a UAV to acquire images over a winter wheat fertilization experiment. The acquired images were processed by Pix4D and Photoscan software packages to produce mosaicked image. In the meanwhile, single multispectral images were also produced using the method released by sensor manufacturer. Based on above data, randomly placed samples in the fertilization treatment, extracted spectral information of each point in the three types of images individually, and compared their reflectance value and image contrast. Although there were significant correlations in reflectance values between the mosaicked images proceed by Pix4D and Photoscan and the single image for each band, the reflectance value and its coefficients of variation from Photoscan mosaicked image were closer to the corresponding values of single image, compared with Pix4D mosaicked image. It means Photoscan retains more spectral information of the original image during stitching image. Considering the geometric accuracy results and software package prices from existing studies, Photoscan is suggested as the better package for users.

Key words: unmanned aerial vehicle, orthomosaic, Photoscan, Pix4D, agriculture

图1

无人机飞行平台及获取的一景影像"

图2

样点空间分布图"

图3

数据分析流程图"

图4

Pix4D拼接影像(a)、Photoscan拼接影像(b)、一些单张影像(c)的假彩色合成影像和小区施氮量空间分布图(d)"

图5

单张影像与拼接影像在各波段的反射率散点图 (a)蓝光波段; (b)绿光波段; (c)红光波段; (d)红边波段; (e)近红外波段。图中虚线表示1:1线。"

表1

Pix4D拼接影像、Photoscan拼接影像和单张影像之间反射率成对数据t检验结果"

波段
Channel		 Pix4D 拼接影像vs.
Photoscan拼接影像
Pix4D mosaicked image vs. Photoscan mosaicked image		 Pix4D 拼接影像vs.单张影像
Pix4D mosaicked image vs. single image		 Photoscan拼接影像vs.单张影像
Photoscan mosaicked image vs. single image
蓝光波段Blue band < 0.001*** < 0.001*** 0.043*
绿光波段Green band < 0.001*** < 0.001*** 0.003**
红光波段Red band < 0.001*** < 0.001*** 0.425 ns
红边波段Red edge band < 0.001*** < 0.001*** < 0.001***
近红外波段Near infrared band < 0.001*** < 0.001*** < 0.001***

表2

Pix4D拼接影像、Photoscan拼接影像和单张影像样点处光谱反射率平均变异系数(CV)"

波段
Channel		 Pix4D拼接影像
Pix4D mosaicked image		 Photoscan拼接影像
Photoscan mosaicked image		 单张影像
Single image
蓝光波段 Blue band 0.1585 0.1587 0.1677
绿光波段 Green band 0.1287 0.1369 0.1404
红光波段 Red band 0.2042 0.2095 0.2105
红边波段 Red edge band 0.0853 0.0930 0.0982
近红外波段 Near infrared band 0.1073 0.1125 0.1173

图6

单张影像与拼接影像在各波段的反射率变异系数散点图 (a)蓝光波段; (b)绿光波段; (c)红光波段; (d)红边波段; (e)近红外波段。图中虚线表示1:1线。"

表3

Pix4D拼接影像、Photoscan拼接影像和单张影像之间反射率变异系数的成对数据t检验结果"

波段
Channel		 Pix4D拼接影像vs.
Photoscan拼接影像
Pix4D mosaicked image vs. Photoscan mosaicked image		 Pix4D 拼接影像vs.
单张影像
Pix4D mosaicked image vs. single image		 Photoscan拼接影像vs.
单张影像
Photoscan mosaicked image vs.
single image
蓝光波段Blue band 0.823 ns 0.003** 0.003**
绿光波段Green band < 0.001*** < 0.001*** 0.009**
红光波段Red band < 0.001*** 0.046* 0.759 ns
红边波段Red edge band < 0.001*** < 0.001*** < 0.001***
近红外波段Near infrared band < 0.001*** < 0.001*** 0.047*
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