摘要：
Honghu Lake, one of the seven largest fresh-water lakes in China, is well known for its ecological and economic importance, as well as its rapid changes in recent years. This study investigates the potential of using remote sensing to map and monitor aquatic vegetation changes in Honghu Lake on a large scale. Landsat TM/ETM+ images dated July 27, 2000, July 9, 2002, and July 17, 2008, and CBERS image dated August 12, 2005, are employed to map the aquatic vegetation distribution in the lake. A hybrid classification method, combining the power of the decision tree classifier, naive Bayes classifier, and supporting vector machine classifier is used to distinguish different wetland types. A novel polar coordinate map method is proposed to map the changes of aquatic vegetation on a large scale. The map demonstrates vegetation patch size changes and percentage changes in the whole lake directions during four periods. Validation using in situ surveys and historical ancillary data suggests that this approach could map the distribution and monitor the changes of aquatic vegetation on a large scale efficiently. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI:10.1117/1.JRS.7.073593]
作者机构：
[李畅] College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China;[李芳芳] Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
通讯机构：
[Li, C.] C;College of Urban and Environmental Science, Central China Normal University, China
期刊：
Telkomnika (Telecommunication Computing Electronics and Control),2013年11(12):7462-7469 ISSN：1693-6930
通讯作者：
Li, C.(lichang@mail.ccnu.edu.cn)
作者机构：
[Chang LI] Key Laboratory of Disaster Reduction, Emergency Response Engineering of the Ministry of Civil Affairs, 6 Guangbai East Road, Chaoyang District, Beijing, 100124, China;[Fangfang LI] College of Information Systems and Management, China National University of Defense Technology, Changsha 410073, China;[Wenzhong SHI] Joint Spatial Information Research Laboratory, The Hong Kong Polytechnic University and Wuhan University, Hong Kong and Wuhan, China;[Chang LI] College of Urban and Environmental Science, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China
摘要：
Current methods of remotely sensed image change detection almost assume that the DEM of the surface objects do not change. However, for the geological disasters areas (such as: landslides, mudslides and avalanches, etc.), this assumption does not hold. And the traditional approach is being challenged. Thus, a new theory for change detection needs to be extended from two-dimensional (2D) to three-dimensional (3D) urgently. This paper aims to present an innovative scheme for change detection method, object-oriented simultaneous three-dimensional geometric and physical change detection (OOS3DGPCD) using GIS-guided knowledge. This aim will be reached by realizing the following specific objectives: a) to develop a set of automatic multi-feature matching and registration methods; b) to propose an approach for simultaneous detecting 3D geometric and physical attributes changes based on the object-oriented strategy; c) to develop a quality control method for OOS3DGPCD; d) to implement the newly proposed OOS3DGPCD method by designing algorithms and developing a prototype system. For aerial remotely sensed images of YingXiu, Wenchuan, preliminary experimental results of 3D change detection are shown so as to verify our approach.
期刊：
Journal of Convergence Information Technology,2012年7(19):546-553 ISSN：1975-9320
通讯作者：
Li, F.(lifangfang83@163.com)
作者机构：
[Li, Fangfang] Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China;[Mao, Xingliang] Internet News Management Office of Publicity, Department of Hunan Provincial CCP Committees, Changsha 410011, China;[Xiao, Benlin] Civil Engineering and Architecture School, Hubei University of technology, Wuhan 430068, China;[Li, Chang] College of Urban and Environmental Science, HuaZhong Normal University, Wuhan 430079, China
摘要：
Aquatic vegetation plays an important role in the maintenance of wetland biodiversity and ecological function. As the complex spectral characteristics and growth environment, its spatial distribution is affected by many factors. This study investigated the potential of using remote sensing to map aquatic vegetation distribution on a large scale in Honghu Lake, China. According to aquatic vegetation's ecological characteristics, the study firstly analyzed the selection and extraction of optimal feature images benefiting aquatic vegetation classification. Next, classification knowledge mining based on these feature images was discussed. Finally, a multi-classifier combination method, which combines decision tree classifier, naive bayes classifier and supporting vector machine classifier, was proposed to distinguish different wetland types. Validation using in situ surveys suggested that this approach could get higher accuracy than each single classifier in mapping aquatic vegetation distribution on a large scale.
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：
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.
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
