高分辨率遥感影像DSM的改进半全局匹配生成方法

doi:10.11947/j.AGCS.2018.20180091

测绘学报 ›› 2018, Vol. 47 ›› Issue (10): 1372-1384.doi: 10.11947/j.AGCS.2018.20180091

高分辨率遥感影像DSM的改进半全局匹配生成方法

杨幸彬¹, 吕京国^1,2, 江珊¹, 张丹璐¹

1. 北京建筑大学测绘与城市空间信息学院, 北京 100044;
2. 城市空间信息工程北京市重点实验室, 北京 100044

收稿日期:2018-03-09 修回日期:2018-07-08 出版日期:2018-10-20 发布日期:2018-10-24
通讯作者: 吕京国 E-mail:lvjg@bucea.edu.cn
作者简介:杨幸彬(1991-),男,硕士生,研究方向为影像密集匹配与三维重建。E-mail:1126779429@qq.com
基金资助:
国家重点研发计划（2016YFB0500304）；城市空间信息工程北京市重点实验室基金（2017212）；城市设计大数据获取与处理高精尖项目（30059917306）

Digital Surface Model Generation for High Resolution Satellite Stereo Image Based on Modified Semi-global Matching

YANG Xingbin¹, LÜ Jingguo^1,2, JIANG Shan¹, ZHANG Danlu¹

1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100044, China

Received:2018-03-09 Revised:2018-07-08 Online:2018-10-20 Published:2018-10-24
Supported by:
The National Key Research and Development Program of China (No. 2016YFB0500304);The Fund of Beijing Key Laboratory of Urban Spatial Information Engineering (No. 2017212);The Advanced Project of Urban Design Big Data Acquisition and Processing(30059917306)

摘要/Abstract

摘要： 提出一种基于改进半全局匹配算法的高分辨率遥感影像数字表面模型（digital surface model，DSM）生成方法。首先利用影像间连接点几何约束关系对有理函数模型进行系统误差补偿，在补偿模型的基础上对影像进行分块，利用投影轨迹法逐块得到核线影像对；在密集匹配阶段，对影像建立金字塔后逐层进行半全局匹配，匹配过程中引入顾及影像纹理信息的视差图膨胀腐蚀算法约束每一层视差搜索范围，增加了视差图边缘处的有效像素数，同时减少了算法所需的内存开销和计算时间；在视差图后处理阶段，利用加权中值滤波算法保护了视差图的边缘信息；最后基于前方交会获取DSM。选取WorldView 3和资源三号立体影像进行试验，结果表明，本文方法获取的DSM精度在高程方向上接近于1.5倍GSD，并且较好地保持了地物的边缘特性，在计算效率和内存开销方面也具有较好的平衡。

关键词: 高分辨率遥感影像, 有理函数模型, 投影轨迹, 半全局匹配, 数字表面模型

Abstract: A method is proposed for generating digital surface model (DSM) of high resolution satellite imagery (HRSI) based on modified semi-global matching (SGM) algorithm.Firstly,the system error of the rational function model is compensated by using the geometric constraint relation between the image connection points.Based on the compensation model,the image is divided into blocks.The projection trajectory method is used to obtain the image pairs of the images.In the dense matching stage,the disparity map is computed using semi-global matching by layer after building the pyramids images,and an expansion corrosion algorithm for disparity graphs,which takes into account the image texture information,is introduced to constrain the range of parallax search,increase the number of effective pixels at the edge of the parallax map and reduce the memory overhead and computation time required for the algorithm.In the post processing stage of disparity image,the edge information of disparity image is protected by weighted median filtering algorithm.Finally,the DSM is acquired based on the forward intersection.The stereo images of World View 3 and ZY-3 to experiment are selected.The experimental results show that the DSM accuracy obtained by this method is nearly 1.5 times higher than that of GSD in elevation direction,and the edge characteristics of the object are maintained well.The algorithm is computationally efficient and has relatively low memory overhead.

Key words: high resolution satellite imagery, rational function model, projection trajectory, semi-global matching, digital surface model

中图分类号:

P236

杨幸彬, 吕京国, 江珊, 张丹璐. 高分辨率遥感影像DSM的改进半全局匹配生成方法[J]. 测绘学报, 2018, 47(10): 1372-1384.

YANG Xingbin, LÜ Jingguo, JIANG Shan, ZHANG Danlu. Digital Surface Model Generation for High Resolution Satellite Stereo Image Based on Modified Semi-global Matching[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(10): 1372-1384.

参考文献

[1] SCHARSTEIN D, SZELISKI R. A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms[J]. International Journal of Computer Vision, 2002, 47(1-3):7-42.
[2] KOLMOGOROV V, MONASSE P, TAN P. Kolmogorov and Zabih's Graph Cuts Stereo Matching Algorithm[J]. Image Processing on Line, 2014(4):220-251.
[3] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Belief Propagation for Early Vision[C]//Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC, USA:IEEE, 2004:261-268.
[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] D'ANGELO P. Improving Semi-global Matching:Cost Aggregation and Confidence Measure[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B1:299-304.
[6] WENZEL K. Dense Image Matching for Close Range Photogrammetry[D]. Stuttgart:University of Stuttgart, 2016.
[7] ROTHERMEL M, WENZEL K, FRITSCH D, et al. SURE:Photogrammetric Surface Reconstruction From Imagery[C]//Proceedings of LC3D Workshop. Berlin:[s.n.], 2012.
[8] KUSCHK G, D'ANGELO P, QIN R, et al. DSM Accuracy Evaluation for the ISPRS Commission I Image Matching Benchmark[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, XL-1:195-200.
[9] D'ANGELO P. Evaluation of ZY-3 for DSM and Ortho Image Generation[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, XL-1/W1:57-61.
[10] GHUFFAR S. Satellite Stereo Based Digital Surface Model Generation Using Semi Global Matching in Object and Image Space[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016(Ⅲ-1):63-68.
[11] BETHMANN F, LUHMANN T. Semi-global Matching in Object Space[J]. ISPRS-international Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, XL-3/W2:23-30.
[12] LUHMANN T, BETHMANN F, HASTEDT H. Dense Pointclouds from Combined Nadir and Oblique Imagery by Object-based Semi-global Multi-image Matching[D]. Oldenburg:Jade University of Applied Sciences, 2017.
[13] HIRSCHMULLER H. Stereo Processing By Semiglobal Matching and Mutual Information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2):328-341.
[14] TATAR N, SAADATSERESHT M, AREFI H, et al. Quasi-epipolar Resampling of High Resolution Satellite Stereo Imagery for Semi Global Matching[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, XL-1-W5:707-712.
[15] 张春森, 吕佩育, 郭丙轩. 基于控制点约束影像的密集匹配及其在考古发掘中的应用[J]. 武汉大学学报(信息科学版), 2015, 40(12):1575-1581. ZHANG Chunsen, LÜ Peiyu, GUO Bingxuan. Based GCP-SGM Algorithm and Its Application in Scene 3D Reconstruction of Archaeological Excavation Site[J]. Geomatics and Information Science of Wuhan University, 2015, 40(12):1575-1581.
[16] 张彦峰. 利用多条件约束的航空影像逐像素密集匹配算法研究[D]. 北京:中国测绘科学研究院, 2014. ZHANG Yanfeng. Pixel-wise Dense Matching of Aerial Images with Multi-conditional Constraints[D]. Beijing:Chinese Academy of Surveying and Mapping, 2014.
[17] ROTHERMEL M. Development of A SGM-based Multi-view Reconstruction Framework for Aerial Imagery[D]. Stuttgart:University of Stuttgart, 2016.
[18] GONG K, FRITSCH D. A Detailed Study about Digital Surface Model Generation Using High Resolution Satellite Stereo Imagery[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, Ⅲ-1:69-76.
[19] 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. 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.
[20] 刘军, 张永生, 王冬红. 基于RPC模型的高分辨率卫星影像精确定位[J]. 测绘学报, 2006, 35(1):30-34. LIU Jun, ZHANG Yongsheng, WANG Donghong. Precise Positioning of High Spatial Resolution Satellite Images Based on RPC Models[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(1):30-34.
[21] 李德仁, 张过, 江万寿, 等. 缺少控制点的SPOT-5 HRS影像RPC模型区域网平差[J]. 武汉大学学报(信息科学版), 2006, 31(5):377-381. LI Deren, ZHANG Guo, JIANG Wanshou, et al. SPOT-5 HRS Satellite Imagery Block Adjustment Without GCPS or with Single GCP[J]. Geomatics and Information Science of Wuhan University, 2006, 31(5):377-381.
[22] MORGAN M, KIM K O, JEONG S, et al. Epipolar Resampling of Space-borne Linear Array Scanner Scenes Using Parallel Projection[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(11):1255-1263.
[23] 张过, 潘红播, 江万寿, 等. 基于RPC模型的线阵卫星影像核线排列及其几何关系重建[J]. 国土资源遥感, 2010, 22(4):1-5. ZHANG Guo, PAN Hongbo, JIANG Wanshou, et al. Epipolar Resampling and Epipolar Geometry Reconstruction of Linear Array Scanner Scenes Based on RPC Model[J]. Remote Sensing for Land & Resources, 2010, 22(4):1-5.
[24] HIRSCHMULLER H, SCHARSTEIN D. Evaluation of Cost Functions for Stereo Matching[C]//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA:IEEE, 2007:1-8.
[25] HUMENBERGER M, ENGELKE T, KUBINGER W. A Census-based Stereo Vision Algorithm Using Modified Semi-global Matching and Plane Fitting to Improve Matching Quality[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops. San Francisco, CA, USA:IEEE, 2010:77-84.
[26] SPANGENBERG R, LANGNER T, ADFELDT S, et al. Large Scale Semi-global Matching on the CPU[C]//Proceedings of 2014 IEEE Intelligent Vehicles Symposium. Dearborn, MI, USA:IEEE, 2014:195-201.
[27] 袁修孝, 曹金山. 高分辨率卫星遥感精确对地目标定位理论与方法[M]. 北京:科学出版社, 2012:182-183. YUAN Xiuxiao, CAO Jinshan. Theory and Method of Precise Object Positioning of High Resolution Satellite Imagery[M]. Beijing:Science Press, 2012:182-183.

高分辨率遥感影像DSM的改进半全局匹配生成方法

Digital Surface Model Generation for High Resolution Satellite Stereo Image Based on Modified Semi-global Matching

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