欢迎访问作物学报,今天是

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 发布日期:2020-01-28
  • 通讯作者: 陈鹏飞 E-mail:pengfeichen@igsnrr.ac.cn
  • 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 Online:2020-07-12 Published:2020-01-28
  • Contact: Peng-Fei CHEN E-mail:pengfeichen@igsnrr.ac.cn
  • 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)

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*
[1] Chen P, Haboudane D, Tremblay N, Wang J, Vigneault P, Li B. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens Environ, 2010,114:1987-1997.
doi: 10.1016/j.rse.2010.04.006
[2] 吴琼, 齐波, 赵团结, 姚鑫锋, 朱艳, 盖钧镒. 高光谱遥感估测大豆冠层生长和籽粒产量的探讨. 作物学报, 2013,39:309-318.
doi: 10.3724/SP.J.1006.2013.00309
Wu Q, Qi B, Zhao T J, Yao X F, Zhu Y, Gai J Y. A tentative study on utilization of canopy hyperspectral reflectance to estimate canopy growth and seed yield in soybean. Acta Agron Sin, 2013,39:309-318 (in Chinese with English abstract).
doi: 10.3724/SP.J.1006.2013.00309
[3] John R, Chen J Q, Giannico V, Park H, Xiao J F, Shirkey G, Ouyang Z T, Shao C L, Lafortezza R, Qi J G. Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors. Remote Sens Environ, 2018,213:34-48.
doi: 10.1016/j.rse.2018.05.002
[4] Rumpler M, Daftry S, Tscharf A, Prettenthaler R, Hoppe C, Mayer G, Bischof H. Automated end-to-end workflow for precise and geo-accurate reconstractions using fiducial markers. ISPRS Ann Photogramm Remote Sens Spat Inf Sci, 2014, II-3:135-142.
[5] 陈鹏飞. 无人机在农业的应用现状与展望. 浙江大学学报(农业与生命科学版), 2018,44:399-406.
Chen P F. Application status and prospect of UAV in agriculture. J Zhejiang Univ (Agric Life Sci), 2018,44:399-406 (in Chinese with English abstract).
[6] 李德仁, 李明. 无人机遥感系统的研究进展与应用前景. 武汉大学学报(信息科学版), 2014,39:505-513.
doi: 10.13203/j.whugis20140045
Li D R, Li M. Research progress and application prospect of UAV remote sensing system. J Wuhan Univ (Inf Sci Edn), 2014,39:505-513 (in Chinese with English abstract).
doi: 10.13203/j.whugis20140045
[7] Aasen H, Burkart A, Bolten A, Bareth G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J Photogramm Remote Sens, 2015,108:245-259.
[8] 高林, 杨贵军, 李长春, 冯海宽, 徐波, 王磊, 董锦绘, 付奎. 基于光谱特征与PLRS结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用. 作物学报, 2017,43:549-557.
Gao L, Yang G J, Li C C, Feng H K, Xu B, Wang L, Dong J H, Fu K. Application of an improved method in retrieving leaf area index combined spectral index with PLSR in hyperspectral data generated by unmanned aerial vehicle snapshot camera. Acta Agron Sin, 2017,43:549-557 (in Chinese with English abstract).
[9] Shafian S, Rajan N, Schnell R, Bagavathiannan M, Valasek J. Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PLoS One, 2018,13:e0196605.
pmid: 29715311
[10] Zhou G. Near real-time orthorectification and mosaic of small UAV video flow for time-critical event response. IEEE Trans Geosci Remote Sens, 2009,47:739-747.
doi: 10.1109/TGRS.2008.2006505
[11] 董梅, 苏建东, 杨举田, 刘广玉, 李乃会, 黄泽祥, 田雷. 大区域无人机影像快速无缝拼接方法. 测绘科学, 2014,39(11):129-132.
Dong M, Su J D, Yang J T, Liu G Y, Li N H, Huang Z Y, Tian L. A fast seamless image mosaic method for UAV images in large areas. Sci Surv Map, 2014,39(11):129-132 (in Chinese with English abstract).
[12] 王欢, 蒋显岚. 4种无人机遥感影像快速拼接方法的试验分析. 测绘与空间地理信息, 2015,38(8):117-118.
Wang H, Jiang X L. Experimental analysis of 4 kind of remote sensing image mosaic method for UAV. Geomat Spat Inf Technol, 2015,38(8):117-118.
[13] Turner D, Lucieer A, Watson C. An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point Clouds. Remote Sens, 2012,4:1392-1410.
doi: 10.3390/rs4051392
[14] Gross J W, Heumann B W. A statistical examination of image stitching software packages for use with unmanned aerial systems. Photogramm Eng Remote Sens, 2016,82:419-425.
doi: 10.14358/PERS.82.6.419
[15] Barazzetti L, Remondino F, Scaioni M. Automation in 3D reconstruction: Results on different kinds of close-range blocks. Int Arch Photogramm Remote Sens Spat Inf Sci, 2010,38:55-61.
[16] Westoby M, Brasington J, Glasser N, Hambrey M, Reynolds J. Structure-from-motion photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology, 2012,179:300-314.
[17] Vasuki Y, Holden E J, Kovesi P, Micklethwaite S. Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Comput Geosci (UK), 2014,69:22-32.
doi: 10.1016/j.cageo.2014.04.012
[18] Sona G, Pinto L, Pagliari D, Passoni D, Gini R. Experimental analysis of different software packages for orientation and digital surface modeling from UAV images. Earth Sci Inf, 2014,7:97-107.
[19] Turner D, Lucieer A, Wallace L. Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Trans Geosci Remote Sens, 2014,52:2738-2745.
doi: 10.1109/TGRS.36
[1] 高超,李学文,孙艳伟,周婷,罗纲,陈财. 淮河流域夏玉米生育阶段需水量及农业干旱时空特征[J]. 作物学报, 2019, 45(2): 297-309.
[2] 高林,杨贵军,李长春,冯海宽,徐波,王磊,董锦绘,付奎. 基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用[J]. 作物学报, 2017, 43(04): 549-557.
[3] 陈彦清,曹永生,方沩*,陈丽娜. 综合农业分区尺度下农作物种质资源的空间分布特征[J]. 作物学报, 2017, 43(03): 378-388.
[4] 史磊刚, 范士超, 孔凡磊, 陈阜. 华北平原主要作物生产的碳效率研究初报[J]. 作物学报, 2011, 37(08): 1485-1490.
[5] 金之庆;石春林;葛道阔;高亮之;杨星卫;薛正平;陆贤;丁美花. 基于RCSODS的直播水稻精确施氮模拟模型[J]. 作物学报, 2003, 29(03): 353-359.
[6] 王克晶;余建章;李福山. 我国大豆种皮过氧化物酶活性和根部荧光性基因表型频率分布[J]. 作物学报, 1990, 16(03): 276-283.
[7] 俞履圻. 读《印度农业史》,评普通栽培稻种起源问题[J]. 作物学报, 1989, 15(01): 86-93.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李绍清;李阳生;吴福顺;廖江林;李达模. 水稻孕穗期在淹涝胁迫下施肥的优化选择及其作用机理[J]. 作物学报, 2002, 28(01): 115 -120 .
[2] 王兰珍;米国华;陈范骏;张福锁. 不同产量结构小麦品种对缺磷反应的分析[J]. 作物学报, 2003, 29(06): 867 -870 .
[3] 胡希远;李建平;宋喜芳. 空间统计分析在作物育种品系选择中的效果[J]. 作物学报, 2008, 34(03): 412 -417 .
[4] 郑希;吴建国;楼向阳;徐海明;石春海. 不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析[J]. 作物学报, 2008, 34(03): 369 -375 .
[5] 邢光南, 周斌, 赵团结, 喻德跃, 邢邯, 陈受宜, 盖钧镒. 大豆抗筛豆龟蝽Megacota cribraria (Fabricius)的QTL分析[J]. 作物学报, 2008, 34(03): 361 -368 .
[6] 柯丽萍;郑滔;吴学龙;何海燕;陈锦清. 甘蓝型油菜SLG基因片段的克隆及序列分析[J]. 作物学报, 2008, 34(05): 764 -769 .
[7] 郑永美;丁艳锋;王强盛;李刚华;王惠芝;王绍华. 起身肥对水稻分蘖和氮素吸收利用的影响[J]. 作物学报, 2008, 34(03): 513 -519 .
[8] 吕丽华;陶洪斌;夏来坤; 张雅杰; 赵明; 赵久然;王璞. 不同种植密度下的夏玉米冠层结构及光合特性[J]. 作物学报, 2008, 34(03): 447 -455 .
[9] 梁太波;尹燕枰;蔡瑞国;闫素辉;李文阳;耿庆辉;王平;王振林. 大穗型小麦品种强、弱势籽粒淀粉积累和相关酶活性的比较[J]. 作物学报, 2008, 34(01): 150 -156 .
[10] 张书标;杨仁崔. e-杂交稻若干生物学特性研究[J]. 作物学报, 2003, 29(06): 919 -924 .