Automatic generation DSM of UAV image based on random propagation COLVLL algorithm

doi:10.11947/j.AGCS.2022.20210325

Abstract

Abstract: In view of the poor performance of existing dense matching methods in weak texture areas and areas with large height differences, and the loss of information when the dense matching results are fused to generate DSM, a DSM generation method based on random propagation COLVLL is proposed. Based on the effective image pair screening of the images after aerial triangulation photogrammetry, the random propagation mechanism is used to scan and iterate the DSM pixel area, combined with the VLL algorithm to iteratively update the randomly generated elevation value to obtain the DSM. Taking the UAV image with weak texture and large elevation difference as the experimental data, compare with the commercial software for generating DSM, and use the Vaihingen data set provided by ISPRS WGII/4 as a reference to test and analyze the DSM and real radiographic data generated by the method in this paper. The results show the effectiveness and applicability of the proposed method.

Key words: DSM, UAV image, dense matching, VLL algorithm, object patch

CLC Number:

P231

ZHANG Chunsen, GE Yingwei, GUO Bingxuan, ZHANG Yueying. Automatic generation DSM of UAV image based on random propagation COLVLL algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11): 2346-2354.

References

[1] 方稚, 杨本勇, 谢宇. 基于资源三号影像的大范围DSM和DOM快速生产方法研究[J]. 测绘, 2017, 40(6): 254-256, 259. FANG Zhi, YANG Benyong, XIE Yu. Research on large-scale DSM and DOM rapid production method based on Ziyuan No.3 image[J]. Surveying and Mapping, 2017, 40(6): 254-256, 259.
[2] 张春森, 张奇源, 郭丙轩, 等. 倾斜影像自适应初始物方面元优化匹配算法[J]. 测绘学报, 2020, 49(1): 108-116. DOI:10.11947/j.AGCS.2020.20190050. ZHANG Chunsen, ZHANG Qiyuan, GUO Bingxuan, et al. Oblique image matching algorithm based on adaptive initial object patch[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(1): 108-116. DOI:10.11947/j.AGCS.2020.20190050.
[3] 林森, 殷新勇, 唐延东. 双目视觉立体匹配技术研究现状和展望[J]. 科学技术与工程, 2017, 17(30): 135-147. LIN Sen, YIN Xinyong, TANG Yandong. Research status and prospect of binocular stereo matching technology[J]. Science Technology and Engineering, 2017, 17(30): 135-147.
[4] HIRSCHMULLER H. Accurate and efficient stereo processing by semi-global matching and mutual information[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005: 807-814.
[5] 闫利, 费亮, 陈长海, 等. 利用网络图进行高分辨率航空多视影像密集匹配[J]. 测绘学报, 2016, 45(10): 1171-1181. DOI: 10.11947/j.AGCS.2016.20160068. YAN Li, FEI Liang, CHEN Changhai, et al. A multi-view dense matching algorithm of high resolution aerial images based on graph network[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(10): 1171-1181. DOI: 10.11947/j.AGCS.2016.20160068.
[6] 郑顺义, 张祖勋, 张剑清. 基于物方影像匹配和概率松弛的断面自动提取[J]. 测绘地理信息, 2004, 29(2): 26-27. ZHENG Shunyi, ZHANG Zuxun, ZHANG Jianqing. Automatic profile extraction based on object space image matching and probability relaxation algorithm[J]. Journal of Geomatics, 2004, 29(2): 26-27.
[7] ZHANG Li, GRUEN A. Multi-image matching for DSM generation from IKONOS imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2006, 60(3): 195-211.
[8] 纪松,范大昭,张永生,等.多视匹配MVLL算法及其在ADS40线阵影像中的运用[J].武汉大学学报(信息科学版), 2009,34(1): 28-31. JI Song, FAN Dazhao,ZHANG Yongsheng, et al. MVLL multi-image matching model and its application in ADS40 linear array images [J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 28-31.
[9] 王竞雪,朱庆,王伟玺.多匹配基元集成的多视影像密集匹配方法[J].测绘学报, 2013,42(5):691-698. WANG Jingxue, ZHU Qing, WANG Weixi.A dense matching algorithm of multi-view image based on the integrated multiple matching primitives [J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5): 691-698.
[10] SCHÖNBERGER J L, PRICE T, SATTLER T, et al. A vote-and-verify strategy for fast spatial verification in image retrieval[C] //Proceedings of 2016 Asian Conference on Computer Vision. Cham, Switzerland: ACCV, 2016: 321-337.
[11] JOHANNES L, SCHÖNBERGER J L. Robust methods for accurate and efficient 3D modeling from unstructured imagery[D]. North Carolina, USA: University of North Carolina at Chapel Hill, 2018.
[12] BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch: a randomized correspondence algorithm for structural image editing[C]//Proceedings of 2009 ACM Transactions on Graphics. Louisiana, USA: ACM press, 2009, 28: 1-11.
[13] BLEYER M, RHEMANN C, ROTHER C. PatchMatch stereo-stereo matching with slanted support windows[C]//Proceedings of 2011 British Machine Vision Conference. Dundee, Scotland: British Machine Vision Association, 2011: 1-11.
[14] 龚健雅,季顺平.摄影测量与深度学习[J].测绘学报2018,47(6):693-704.DOI:10.11947/j.AGCS.2018.20170640. GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704. DOI:10.11947/j.AGCS.2018.20170640.
[15] 季顺平,罗冲,刘瑾.基于深度学习的立体影像密集匹配方法综述[J].武汉大学学报(信息科学版),2021,46(2):193-202. DOI: 10.13203/j.whugis20200620. JI Shunping, LUO Chong, LIU Jin. A review of dense stereo image matching methods based on deep learning[J]. Geomatics and Information Science of Wuhan University, 2021, 46(2): 193-202. DOI: 10.13203/j.whugis20200620.
[16] LYU Jingguo, YANG Xingbin, ZHANG Danlu, et al. High-resolution remote sensing image semi-global matching method considering geometric constraints of connection points and image texture information[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4): 97-112.
[17] FAN Dazhao, DONG Yang, ZHANG Yongsheng. Satellite image matching method based on deep convolutional neural network[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2): 92-102.