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REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (4) : 179-182     DOI: 10.6046/gtzyyg.2015.04.27
Technology Application |
UAV imagery data processing for emergency response based on PhotoScan Pro
ZHAO Yunjing, GONG Xucai, DU Wenjun, ZHOU Li
Yunnan Basic Surveying and Mapping Technology Center, Kunming 650034, China
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Abstract  

Due to irregular flight route, low image quality and unstable flight attitude, it is really difficult to perform UAV imagery data processing using traditional photogrammetric software. For emergency response, a stable,highly automatic and time-efficient software solution is always highly demanded. In this paper, the authors present a UAV imagery solution for emergence using Agisoft PhotoScan Pro software to offer orthomosaic and dense point cloud service. It is held that this method can provide timely and effective scientific image for emergence, as shown by several emergency response practical applications.

Keywords land surface temperature      FY-2C      split-window algorithm      MODIS     
:  TP79  
Issue Date: 23 July 2015
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WANG Yawei
SONG Xiaoning
TANG Bohui
LI Zhaoliang
LENG Pei
Cite this article:   
WANG Yawei,SONG Xiaoning,TANG Bohui, et al. UAV imagery data processing for emergency response based on PhotoScan Pro[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 179-182.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.04.27     OR     https://www.gtzyyg.com/EN/Y2015/V27/I4/179

[1] 中测瑞格测量技术(北京)有限公司.Agisoft Photoscan摄影测量与建模软件——基于数码影像的三维模型重现[EB/OL].[2014-06-01].http://www.ilidar.com/productdetail.aspx?Id=134&ac=product&pid=345. RedChina Geosystems Group(Beijing)Co., Ltd.Agisoft Photoscan photogrammetry and modeling software——structure based on digital imaging[EB/OL].[2014-06-01].http://www.ilidar.com/productdetail.aspx?Id=134&ac=product&pid=345.

[2] 宋丹.尺度不变特征变换匹配算法[EB/OL].http://www.doc88.com/p-49613031067.html

[2011] . Song D.Scale invariant feature transform[EB/OL].http://www.doc88.com/p-49613031067.html

[2011] .

[3] Fischler M A, Bolles R.随机采样一致算法——一种新的可适用于影像分析和制图的模型拟合算法[J].计算机辅助施工管理, 1981, 24(6):381-395. Fischler M A, Bolles R C.Random sample consensus:A paradigm for model fitting with applications to image analysis and cartography[J].CACM, 1981, 24(6):381-395.

[4] Agisoft入门教程:运用Agisoft PhotoScan专业版软件生成正射影像和数字高程模型(无控制点)[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/05/. Agisoft tutorial for beginners:Orthophoto & DEM generation(without GCPs)with Agisoft photoScan Professional[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/05/.

[5] 运用Agisoft PhotoScan专业版软件进行面积与体积量算[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/03/. Agisoft area & volume measurements with Agisoft photoscan professional[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/03/.

[6] 运用Agisoft PhotoScan专业版软件进行密集点云分类和生成数字地面模型[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/08/. Agisoft dense cloud classification & DTM generation with Agisoft photoscan professional[EB/OL].[2014-06-01].http://www.agisoft.ru/tutorials/photoscan/08/.

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