摄影测量学与遥感

正射影像镶嵌线自动搜索的视差图算法

  • 袁修孝 ,
  • 段梦梦 ,
  • 曹金山
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  • 1. 武汉大学遥感信息工程学院, 湖北 武汉 430079;
    2. 地球空间信息技术协同创新中心, 湖北 武汉 430079;
    3. 武汉大学资源与环境科学学院, 湖北 武汉 430079
袁修孝(1963-),男,博士,教授,博士生导师,珞珈杰出学者,主要从事航空航天遥感高精度对地目标定位理论与方法、高分辨率卫星遥感影像几何处理等研究与教学工作。E-mail:yuanxx@whu.edu.cn

收稿日期: 2014-08-08

  修回日期: 2014-12-23

  网络出版日期: 2015-09-02

基金资助

国家973计划(2012CB719902);国家自然科学基金(41371432);国家高分辨率对地观测系统重大专项(50-H31D01-0508-13/15)

A Seam Line Detection Algorithm for Orthophoto Mosaicking Based on Disparity Image

  • YUAN Xiuxiao ,
  • DUAN Mengmeng ,
  • CAO Jinshan
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  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
    2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;
    3. School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China

Received date: 2014-08-08

  Revised date: 2014-12-23

  Online published: 2015-09-02

Supported by

The National Basic Research Program of China(973 Program)(No.2012CB719902);The National Natural Science Foundation of China(No. 41371432);Major Projects of High-resolution Earth Observation System(No.50-H31D01-0508-13/15)

摘要

提出了一种基于正射影像视差图的区域级镶嵌线搜索算法。首先利用半全局约束立体匹配算法(semi-global matching,SGM)分别计算立体像对的左右视差图,并通过自适应阈值化去除细小的噪声区域,再经数学形态学方法进一步削弱噪声影响和填补小的漏洞区域,得到了较为精细的房屋等非地面区域的分割结果,从而分离出地面与非地面区域;然后采用改进的贪婪蛇搜索算法进行镶嵌线搜索,以提高算法的稳健性。试验表明,本文算法能很好地避开房屋等明显突出地表的实体,得到不穿越非地面区域的最优路径。

本文引用格式

袁修孝 , 段梦梦 , 曹金山 . 正射影像镶嵌线自动搜索的视差图算法[J]. 测绘学报, 2015 , 44(8) : 877 -883 . DOI: 10.11947/j.AGCS.2015.20140421

Abstract

This paper proposes a regional seam line detection algorithm based on the disparity image of the orthophoto. Firstly, the disparity images of the stereo pair are generated by using the SGM (semi-global matching)method, and the small noises are removed through the adaptive threshold. Then, the mathematical morphology method is used to further eliminate the noises and to fill the small holes on the disparity image in order to accurately separate out the non-ground area. Lastly, the seam line is detected using the improved greedy snake algorithm which with better robustness. The experimental result has shown that our method is effective and is able to acquire an optimized seam line that passed around the entities above the ground.

参考文献

[1] SCHICKLER W, THORPE A. Operational Procedure for Automatic True Orthophoto Generation[J]. ISPRS Commission IV Symposium on GIS——Between Vision and Application, 1998, IAPRS, 32(4): 527-532.
[2] KERSCHNER M. Twin Snakes for Determining Seam Lines in Orthoimage Mosaicking[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(4): 454-461.
[3] KERSCHNER M. Seamline Detection in Colour Orthoimage Mosaicking by Use of Twin Snakes[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2001, 56(1): 53-64.
[4] EFROS A A, Freeman W T. Image Quilting for Texture Synthesis and Transfer[C]//Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques.New York: ACMSIGGRAPH, 2001: 341-346.
[5] DAVIS J. Mosaic of Scenes with Moving Objects[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Santa Barbara, CA: IEEE, 1998: 354-360.
[6] ZHANG Jianqing, SUN Mingwei, ZHANG Zuxun. Automated Seamline Detection for Orthophoto Mosaicking Based on Ant Colony Algorithm[J]. Geomatics and Information Science of Wuhan University, 2009, 34(6): 675-678. (张剑清, 孙明伟, 张祖勋. 基于蚁群算法的正射影像镶嵌线自动选择[J]. 武汉大学学报: 信息科学版, 2009, 34(6): 675-678.)
[7] YUAN Xiuxiao,ZHONG Can.An Improvement of Minimizing Local Maximum Algorithm on Searching Seam Line for Orthoimage Mosaicking[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 199-204. (袁修孝, 钟灿. 一种改进的正射影像镶嵌线最小化最大搜索算法[J]. 测绘学报, 2012, 41(2): 199-204.)
[8] PAN Jun, WANG Mi, LI Deren. Generation of Seamline Network Using Area Voronoi Diagram with Overlap[J]. Geomatics and Information Science of Wuhan University, 2009, 34(5): 518-521. (潘俊, 王密, 李德仁. 基于顾及重叠的面Voronoi图的接缝线网络的生成方法[J]. 武汉大学学报: 信息科学版, 2009, 34(5): 518-521.)
[9] PAN Jun, WANG Mi, LI Deren. Approach for Automatic Generation and Optimization of Seamline Network[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(3): 289-294. (潘俊, 王密, 李德仁. 接缝线网络的自动生成及优化方法[J]. 测绘学报, 2010, 39(3): 289-294.)
[10] OU Xiaoping. Research on Technologies of Automatic Remote Sensing Image Mosaic[D]. Zhengzhou: Information Engineering University, 2013. (欧小平. 遥感影像自动化镶嵌关键技术研究[D]. 郑州: 信息工程大学, 2013.)
[11] ZUO Zhiquan, ZHANG Zuxun, ZHANG Jianqing, et al. Seamlines Intelligent Detection in Large-scale Urban Orthoimage Mosaicking[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(1): 84-89. (左志权, 张祖勋, 张剑清,等. DSM辅助下城区大比例尺正射影像镶嵌线智能检测[J]. 测绘学报, 2011, 40(1): 84-89.)
[12] SUN Jie, MA Hongchao, TANG Xuan. Optimization of LiDAR System Ortho-image Mosaic Seam-line[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 325-328. (孙杰, 马洪超, 汤璇. 机载LiDAR正射影像镶嵌线智能优化研究[J]. 武汉大学学报: 信息科学版, 2011, 36(3): 325-328.)
[13] ZHOU Qinghua, PAN Jun, LI Deren. Overview of Automatic Generation of Mosaicking Seamlines for Remote Sensing Images[J]. Remote Sensing for Land and Resources, 2013, 25(2): 1-7. (周清华, 潘俊, 李德仁. 遥感图像镶嵌接缝线自动生成方法综述[J]. 国土资源遥感, 2013, 25(2): 1-7.)
[14] ZHOU Qinghua. Research on Seamline Optimization for Aerial Images[D]. Wuhan: Wuhan University, 2013. (周清华. 航空影像接缝线优化方法研究[D]. 武汉: 武汉大学, 2013.)
[15] SOILLE P. Morphological Image Compositing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(5): 673-683.
[16] BIELSKI C, GRAZZINI J, SOILLE P. Automated Morphological Image Composition for Mosaicing Large Image Data Sets[C]//IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE, 2007: 4068-4071.
[17] BIELSKI C, SOILLE P. Order Independent Image Compositing[C]//ROLI F,VITULANOS ed. Image Analysis and Processing.Berlin: Springer, 2005, 3617: 1076-1083.
[18] HIRSCHMULLER H. Accurate and Efficient Stereo Processing by Semi-global Matching and Mutual Information[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2005: 807-814.
[19] SUI Lichun, ZHANG Yibin, LIU Yan, et al. Filtering of Airborn LiDAR Point Cloud Data Based on the Adaptive Mathematical Morphology[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4): 390-396. (隋立春, 张熠斌, 柳艳,等. 基于改进的数学形态学算法的LiDAR点云数据滤波[J]. 测绘学报, 2010, 39(4): 390-396.)
[20] GENG Shuai. Image Denoising Based on Mathematical Morphology[D]. Ji'nan: Shandong Normal University, 2012. (耿帅. 基于数学形态学的图像去噪[D]. 济南: 山东师范大学, 2012.)
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