摘要：
POS, integrated by GPS / INS (Inertial Navigation Systems), has allowed rapid and accurate determination of position and attitude of remote sensing equipment for MMS (Mobile Mapping Systems). However, not only does INS have system error, but also it is very expensive. Therefore, in this paper error distributions of vanishing points are studied and tested in order to substitute INS for MMS in some special land-based scene, such as ground façade where usually only two vanishing points can be detected. Thus, the traditional calibration approach based on three orthogonal vanishing points is being challenged. In this article, firstly, the line clusters, which parallel to each others in object space and correspond to the vanishing points, are detected based on RANSAC (Random Sample Consensus) and parallelism geometric constraint. Secondly, condition adjustment with parameters is utilized to estimate nonlinear error equations of two vanishing points (VX, VY). How to set initial weights for the adjustment solution of single image vanishing points is presented. Solving vanishing points and estimating their error distributions base on iteration method with variable weights, co-factor matrix and error ellipse theory. Thirdly, under the condition of known error ellipses of two vanishing points (VX, VY) and on the basis of the triangle geometric relationship of three vanishing points, the error distribution of the third vanishing point (VZ) is calculated and evaluated by random statistical simulation with ignoring camera distortion. Moreover, Monte Carlo methods utilized for random statistical estimation are presented. Finally, experimental results of vanishing points coordinate and their error distributions are shown and analyzed.
作者机构：
[Hu, Min] Chinese PLA Def Informat Acad, Wuhan, Peoples R China.;[Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
会议名称：
The 2011 International Workshop on Internet of Things' Technology and Innovative Application Design(2011年国际物联网技术与创新应用设计研讨会IOT Workshop 2011)
会议时间：
2011-08-24
会议地点：
北京
会议主办单位：
[Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.^[Hu, Min] Chinese PLA Def Informat Acad, Wuhan, Peoples R China.
会议论文集名称：
The 2011 International Workshop on Internet of Things' Technology and Innovative Application Design(2011年国际物联网技术与创新应用设计研讨会IOT Workshop 2011)论文集
关键词：
3S(GPS;cloud computing;digital earth;geo-spatial information;GIS and RS);GPU;grid computing;smart earth
作者机构：
[刘鹏程; 李畅] College of Urban and Environmental Science, Huazhong Normal University, 152 Luoyu Road, Wuhan 430079, China;[李奇] Center for Earth Observation and Digital Earth Airborne Remote Sensing Center, Chinese Academy of Sciences, A 3 Datun Road, Beijing 100101, China;[李芳芳] Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 47 Yanwachizheng Street, Changsha 410073, China
通讯机构：
College of Urban and Environmental Science, Huazhong Normal University, 152 Luoyu Road, China
摘要：
Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.
作者机构：
[罗静; 刘鹏程; 李畅] College of Urban and Environmental Science, Central-China Normal University, 152 Luoyu Road, Wuhan 430079, China;[艾廷华] School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
通讯机构：
College of Urban and Environmental Science, Central-China Normal University, 152 Luoyu Road, China
作者机构：
[李畅] College of Urban and Environmental Science, Huazhong Normal University, Wuhan 430079, China;[张祖勋; 张永军] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
通讯机构：
[Li, C.] C;College of Urban and Environmental Science, Huazhong Normal University, China
作者机构：
[李彩林; 郭宝云] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;[李畅] College of Urban and Environmental Science, Huazhong Normal University, Wuhan 430079, China
通讯机构：
[Li, C.] S;School of Remote Sensing and Information Engineering, Wuhan University, China
摘要：
The disadvantages of convention orthophotos and the difficulty of true orthophotos generation were discussed, then a method to generate true orthophotos was put forward to overcome these disadvantages and difficulty. The process of this method as flows: At first, buildings in the convention orthophotos were corrected. Then, the occulted areas were detected. At last, the missing parts were filled. The data used in this paper include: convention orthophotos, digital elevation model, digital vector graph of buildings and aerial images. Experiments show that the method put forward by this paper is effective.
作者机构：
[李畅] College of Urban and Environmental Science, Huazhong Normal University, 152 Luoyu Road, Wuhan 430079, China;[李奇] Airborne Remote Sensing Center, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, A20 Datun Road, Beijing 100101, China;[李彩林] School of Computer Science, Hubei University of Technology, 1 Lijiadunyi Village, Wuhan 430068, China;[李熙] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
通讯机构：
College of Urban and Environmental Science, Huazhong Normal University, 152 Luoyu Road, China
作者机构：
[Li, Chang; Liu, Pengcheng] Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Yang, Ling] Henan Univ, China Australia Cooperat Res Ctr Geoinformat Anal, Coll Environ & Planning, Kaifeng 475004, Peoples R China.;[Hu, Min] Peoples Liberat Army Commun Command Acad, Wuhan 430010, Peoples R China.;[Hu, Min] China Univ Geosci, Sch Econom & Management, Wuhan 430074, Peoples R China.
通讯机构：
[Li, Chang] H;Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
会议名称：
International Conference on Advanced Measurement and Test (AMT 2010)
会议时间：
MAY 15-16, 2010
会议地点：
Sanya, PEOPLES R CHINA
会议主办单位：
[Li, Chang;Liu, Pengcheng] Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.^[Yang, Ling] Henan Univ, China Australia Cooperat Res Ctr Geoinformat Anal, Coll Environ & Planning, Kaifeng 475004, Peoples R China.^[Hu, Min] Peoples Liberat Army Commun Command Acad, Wuhan 430010, Peoples R China.^[Hu, Min] China Univ Geosci, Sch Econom & Management, Wuhan 430074, Peoples R China.
会议论文集名称：
Key Engineering Materials
关键词：
vanishing point;line clustering;RANSAC;condition adjustment with parameters;error ellipse
摘要：
In close-range digital photogrammetry and computer vision, a major challenge is the automation of 3D reconstruction from 2D-images. And single image calibration is a fundamental task in these areas for research. It is known that camera parameters can be recovered by the vanishing points of three orthogonal directions. However, three reliable and well-distributed vanishing points are not always available. Therefore, how to estimate the error of two vanishing points is very significant for us to analyze the precision of camera calibration. New methods for vanishing point detection and error estimation are presented, which can be illustrated as follows. Firstly, the line clustering, which parallel to object lines and correspond to the vanishing points, are detected based on RANSAC (Random Sample Consensus). Secondly, "condition adjustment with parameters" is utilized to estimate a nonlinear error equation. Thirdly, the error of vanishing point is expressed by error ellipse that is derived by co-factor matrix according to adjustment principle. Finally, experimental results of vanishing points coordinates and their errors are shown and analyzed.
作者机构：
[Li, Chang; Liu, Pengcheng] Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Hu, Min] Peoples Liberat Army Commun Command Acad, Wuhan 430010, Peoples R China.;[Hu, Min] China Univ Geosci, Sch Econom & Management, Wuhan 430074, Peoples R China.
通讯机构：
[Li, Chang] H;Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
会议名称：
International Conference on Advanced Measurement and Test (AMT 2010)
会议时间：
MAY 15-16, 2010
会议地点：
Sanya, PEOPLES R CHINA
会议主办单位：
[Li, Chang;Liu, Pengcheng] Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.^[Hu, Min] Peoples Liberat Army Commun Command Acad, Wuhan 430010, Peoples R China.^[Hu, Min] China Univ Geosci, Sch Econom & Management, Wuhan 430074, Peoples R China.
会议论文集名称：
Key Engineering Materials
关键词：
line extraction;Wallis filtering;LoG;Canny;Hough transform;perceptual organization;LSTM;aerial image
摘要：
Line and plane feature can provide more information than point feature, thus 3D reconstruction based on high-level features, such as line and plane, is an important development trend in Digital Photogrammetry and Computer Vision. Several methods for extracting straight line are researched, and the main procedures can be introduced as follows. Firstly, image is preprocessed by Wallis filtering that is used to enhance the image contrast and reduce the noise, so it is easy to extract more lines. Secondly, Laplacian of Gaussian operator (LoG) and Canny operator algorithms are compared to locate the edge by detecting discontinuity variation in image. Thirdly, Hough transform or perceptual organization based on hypothesis testing are compared and tested for combining and fitting fractured short line segments into a whole line. Lastly, the least square template matching algorithm (LSTM) is done to get higher precise (sub-pixel) located lines. In the experiment, different algorithms for straight line extraction of aerial images are realized and compared, in order to faster achieve richer and higher accurate straight line information, which can pave the way of image understanding and image matching.
