Comparison of UAV remote sensing image processing software for geological disasters monitoring

doi:10.6046/gtzyyg.2016.01.27

Abstract
References
Related Articles
Metrics

Download: PDF(9904 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Unmanned aerial vehicle (UAV) remote sensing has a great application potential in geological disasters monitoring. A plenty of software for UAV image processing has appeared on the market in recent years. In order to provide a valuable reference for choosing UAV image processing software for geological disasters monitoring, the authors evaluated the mostly used software DPGrid, PixelGrid, DPMatrix and Inpho. A block of 644 UAV images of hilly areas acquired in Longnan County of Jiangxi Province by Cannon 5D MARK Ⅱ camera on board a fixed-wing UAV at a flying height of 600 m were used as test data. The preprocessing, aerial triangulation, DEM and DOM generation of these images were performed by one operator using the aforementioned software in the same test environment, then these kinds of software were analyzed in terms of function efficiency, product quality and workflow. The results show that all of these kinds of software have complete UAV image processing capability and the order of processing efficiency from high to low is Inpho, DPMatrix, DPGrid and PixelGrid. For geological disasters emergency survey, it's better to use DPGrid and DPMatrix to generate fast mosaic. For detailed geological disasters survey, all of these kinds of software can meet the requirement while DPGrid and PixelGrid are more suitable for precision control, Inpho and DPGrid are more suitable for brightness balance and color adjustment, and DPGrid is more suitable for editing DEM and DOM.

Keywords land supervision construction land change detection high reliability

TP79

Issue Date: 27 November 2015

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	SUN Fei
	XU Shiwu
	WU Xincai
	XU Shihong

Cite this article:

SUN Fei,XU Shiwu,WU Xincai, et al. Comparison of UAV remote sensing image processing software for geological disasters monitoring[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 183-189.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.01.27 OR https://www.gtzyyg.com/EN/Y2016/V28/I1/183

[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. 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.

[3] Niethammer U,James M R,Rothmund S,et al.UAV-based remote sensing of the Super-Sauze landslide:Evaluation and results[J].Engineering Geology,2012,128:2-11.

[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. 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.

[5] 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(12):1392-1410.

[6] Hardin P J,Jensen R R.Small-scale unmanned aerial vehicles in environmental remote sensing:Challenges and opportunities[J].GIScience & Remote Sensing,2011,48(1):99-111.

[7] 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.

[8] Gini R,Pagliari D,Passoni D,et al.UAV photogrammetry:Block triangulation comparisons[C]//International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,Volume XL-1/W2,2013:157-162.

[9] Remondino F,Del P S,Kersten T P,et al.Low-cost and Open-Source Solutions for Automated Image Orientation:A Critical Overview[M]//Ioannides M,Fritsch D,Leissner J,et al.Progress in Cultural Heritage Preservation,Lecture Notes in Computer Science.Berlin,Heidelberg:Springer,2012:40-54.

[10] Qin R J,Grün A,Huang X F.UAV project-building a reality-based 3D model[J].Coordinates,2013,9(1):18-26.

[11] Turner D,Lucieer A,Wallace L.Direct georeferencing of ultrahigh-resolution UAV imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2738-2745.

[1]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[2]	PAN Jianping, XU Yongjie, LI Mingming, HU Yong, WANG Chunxiao. Research and development of automatic detection technologies for changes in vegetation regions based on correlation coefficients and feature analysis[J]. Remote Sensing for Natural Resources, 2022, 34(1): 67-75.
[3]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[4]	WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
[5]	WANG Xiaolong, YAN Haowen, ZHOU Liang, ZHANG Liming, DANG Xuewei. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea[J]. Remote Sensing for Land & Resources, 2020, 32(4): 163-171.
[6]	XU Rui, YU Xiaoyu, ZHANG Chi, YANG Jin, HUANG Yu, PAN Jun. Building change detection method combining Unet and IR-MAD[J]. Remote Sensing for Land & Resources, 2020, 32(4): 90-96.
[7]	SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
[8]	DIAO Mingguang, LIU Wenjing, LI Jing, LIU Fang, WANG Yanzuo. Dynamic change detection method of vector result data in mine remote sensing monitoring[J]. Remote Sensing for Land & Resources, 2020, 32(3): 240-246.
[9]	REN Xiaoyan, HE Yanfen, WANG Zongming. Spatial-temporal characteristics of construction land expansion and occupation of cultivated land in urban agglomeration of central and southern Liaoning Province based on Remote Sensing[J]. Remote Sensing for Land & Resources, 2020, 32(3): 98-105.
[10]	Chunsen ZHANG, Rongrong WU, Guojun LI, Weihong CUI, Chenyi FENG. High resolution remote sensing image object change detection based on box-plot method[J]. Remote Sensing for Land & Resources, 2020, 32(2): 19-25.
[11]	Yuting YANG, Hailan CHEN, Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.
[12]	Linyan FENG, Bingxiang TAN, Xiaohui WANG, Xinyun CHEN, Weisheng ZENG, Zhao QI. Object-oriented rapid forest change detection based on distribution function[J]. Remote Sensing for Land & Resources, 2020, 32(2): 73-80.
[13]	Haiping WU, Shicun HUANG. Research on new construction land information extraction based on deep learning: Innovation exploration of the national project of land use monitoring via remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(4): 159-166.
[14]	Yizhi LIU, Huarong LAI, Dingwang ZHANG, Feipeng LIU, Xiaolei JIANG, Qing’an CAO. Change detection of high resolution remote sensing image alteration based on multi-feature mixed kernel SVM model[J]. Remote Sensing for Land & Resources, 2019, 31(1): 16-21.
[15]	Zhan ZHAO, Wang XIA, Li YAN. Land use change detection based on multi-source data[J]. Remote Sensing for Land & Resources, 2018, 30(4): 148-155.

Viewed

Full text

Abstract

Cited

Shared

Discussed