Application of UAV low-altitude remote sensing

doi:10.6046/gtzyyg.2017.04.18

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Abstract Unmanned aerial vehicle (UAV) low-altitude remote sensing is an extension and supplement of the traditional aerial photogrammetry, characterized by the airspace application convenience, short launch preparation time, and being less influenced by meteorological conditions, landing site restrictions and regional geological conditions. In order to promote the UAV low-altitude remote sensing technical application, the authors studied its key technologies. The function of UAV low-altitude remote sensing system and the factors considered in the design were analyzed, and the survey process was summarized. A complete technical route of UAV low-altitude remote sensing using in geological survey was formed. To prove the practicability of this technology method, the low-altitude UAV remote sensing system was built up for application in Zhoukoudian area. The results show that this means can provide timely and effective image for geological survey and emergence response survey, and has reference significance for low-altitude UAV remote sensing engineering application.

Keywords hyperspectral remote sensing alteration minerals gold deposit prospecting prediction Beishan

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Issue Date: 04 December 2017

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	REN Guangli
	YANG Min
	LI Jianqiang
	GAO Ting
	LIANG Nan
	YI Huan
	YANG Junlu

Cite this article:

REN Guangli,YANG Min,LI Jianqiang, et al. Application of UAV low-altitude remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 120-125.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.04.18 OR https://www.gtzyyg.com/EN/Y2017/V29/I4/120

[1]　Colomina I,Molina P.Unmanned aerial systems for photogrammetry and remote sensing:A review[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,92:79-97.
[2]　李德仁,李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,39(5):505-513,540.
Li D R,Li M.Research advance and application prospect of unmanned aerial vehicle remote sensing system[J].Geomatics and Information Science of Wuhan University,2014,39(5):505-513,540.
[3]　无人机航测[EB/OL].[2016].http://baike.baidu.com/view/5426384.htm.
UAV aerial photography[EB/OL].[2016].http://baike.baidu.com/view/5426384.htm.
[4]　韩文权,任幼蓉,赵少华.无人机遥感在应对地质灾害中的主要应用[J].地理空间信息,2011,9(5):6-8,163.
Han W Q,Ren Y R,Zhao S H.Primary usages of UAV remote sensing in geological disaster monitoring and rescuing[J].Geospatial Information,2011,9(5):6-8,163.
[5]　何敬,李永树,鲁恒,等.无人机影像地图制作实验研究[J].国土资源遥感,2011,23(4):74-77.doi:10.6046/gtzyyg.2011.04.14.
He J,Li Y S,Lu H,et al.Research on producing image maps based on UAV imagery data[J].Remote Sensing for Land and Resources,2011,23(4):74-77.doi:10.6046/gtzyyg.2011.04.14.
[6]　王玉鹏.无人机低空遥感影像的应用研究[D].焦作:河南理工大学,2011.
Wang Y P.Study on Low-Level Remote Sensing Images of UAV[D].Jiaozuo:Institute of technology of Henan,2011.
[7]　张祖勋,张剑清.数字摄影测量学[M].2版.武汉:武汉大学出版社,2012.
Zhang Z X,Zhang J Q.Photogrammetry[M].2nd ed.Wuhan:Wuhan University Press,2012.
[8]　金鼎坚,支晓栋,王建超,等.面向地质灾害调查的无人机遥感影像处理软件比较[J].国土资源遥感,2016,28(1):183-189.doi:10.6046/gtzyyg.2016.01.27.
Jin D J,Zhi X D,Wang J C,et al.Comparison of UAV remote sensing image processing software for geological disasters monitoring[J].Remote Sensing for Land and Resources,2016,28(1):183-189.doi:10.6046/gtzyyg.2016.01.27.
[9]　赵云景,龚绪才,杜文俊,等.PhotoScan Pro软件在无人机应急航摄中的应用[J].国土资源遥感,2015,27(4):179-182.doi:10.6046/gtzyyg.2015.04.27.
Zhao Y J,Gong X C,Du W J,et al.UAV imagery data processing for emergency response based on PhotoScan Pro[J].Remote Sensing for Land and Resources,2015,27(4):179-182.doi:10.6046/gtzyyg.2015.04.27.
[10] 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[J].Remote Sensing,2012,4(5):1392-1410.
[11] Hardin P J,Jensen R R.Small-scale unmanned aerial vehicles in environmental remote sensing:Challenges and opportunities[J].GIScience and Remote Sensing,2011,48(1):99-111.
[12] Pix4Dmapper软件数据处理操作手册[EB/OL].[2015].http://www.skyway-info.com/cn.
Operation manual for data processing of Pix4Dmapper software[EB/OL].[2015].http://www.skyway-info.com/cn.
[13] Jiang H B,Su Y Y,Jiao Q S,et al.Typical geologic disaster surveying in Wenchuan 8.0 earthquake zone using high resolution ground LiDAR and UAV remote sensing[C]//Lidar remote sensing for environmental monitoring,2014,9262:926219.

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