期刊：
2014 THIRD INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA 2014),2014年:479-483 ISSN：2380-8039
通讯作者：
Hu, Jia
作者机构：
[Hu, Jia; Ling, ChengXing; Zhang, HuaiQing] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China.;[Hu, Jia; Wang, Guangxing; Lin, Hui; Sun, Hua] CSUFT, Res C For RS & Info Engn, Changsha, Hunan, Peoples R China.;[Wang, Guangxing] SIUC, Dept Geog, Carbondale, IL USA.
通讯机构：
[Hu, Jia] C;Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China.
会议名称：
3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA)
会议时间：
JUN 11-14, 2014
会议地点：
Changsha, PEOPLES R CHINA
会议主办单位：
[Hu, Jia;Zhang, HuaiQing;Ling, ChengXing] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China.^[Hu, Jia;Lin, Hui;Sun, Hua;Wang, Guangxing] CSUFT, Res C For RS & Info Engn, Changsha, Hunan, Peoples R China.^[Wang, Guangxing] SIUC, Dept Geog, Carbondale, IL USA.
会议论文集名称：
International Workshop on Earth Observation and Remote Sensing Applications
关键词：
Information extraction;Mean shift segmentation;the East Dongting Lake;Wetland
摘要：
Wetlands are a natural complex formed by the interaction of land and water systems. They play an irreplaceable role in biodiversity conservation, control of global climate change, water purification and mitigation of flood disaster. Thus, extracting information of wetlands has become very important. Recent years the rapid development of high spatial resolution remote sensing technology provides great potential for improvement of data sources and advancing methods for quantitative acquisition and analysis of wetland information. It is well known that object-oriented method is a relatively new technology for landscape segmentation. Although there are some reports in application of object-oriented analysis for extraction of wetland information in China, there is still a lack of studies on the impacts of used segmentation techniques on accuracy of classification. In this study, an excellent image region segmentation method which appeared in the recent years, called mean shift segmentation algorithm, was used to extract the information of wetland in the East Dongting Lake of China and the obtained results were compared with those from a conventional segmentation algorithm provided by ENVI EX. The assessment of the results was conducted using four kinds of quantitative indicators and based on the accuracy of interpretation. The results showed that the conventional segmentation algorithm was unable to provide the accurate segmentation results in delineation of wetland areas. Integrating the edge detection information of NDVI and the mean shift segmentation algorithm not only could make it possible segmentation of shallow water bodies, but also could lead to much better classification results than using the traditional method and the mean shift segmentation alone.
作者机构：
[林辉] Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China;[蒋娴; Zhang H.-Q.] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;[张怀清] Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China, Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
通讯机构：
[Zhang, H.-Q.] R;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, China
作者机构：
[王小明; 李晓靖; 周本智] Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang 311400, Zhejiang, China;[陈柏海; 林辉] Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China;[洪奕丰] Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang 311400, Zhejiang, China, Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China
通讯机构：
[Zhou, B.-Z.] R;Research Institute of Subtropical Forestry, Chinese Academy of Forestry, China
期刊：
Proceedings of 2012 International Conference on Measurement, Information and Control, MIC 2012,2012年1:21-25
通讯作者：
Mo, D.
作者机构：
[Li, Jiping; Yan, Enping; Lin, Hui; Mo, Dengkui] Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China;[Zhang, Guozhen] Research Center of Jianke Landscape, Hunan Academy of Building Research, Changsha, China
会议名称：
2012 International Conference on Measurement,Information and Control(2012测量、信息与控制国际会议 ICMIC2012)
会议时间：
2012-05-18
会议地点：
哈尔滨
会议论文集名称：
2012 International Conference on Measurement,Information and Control(2012测量、信息与控制国际会议 ICMIC2012)论文集
关键词：
hyperspectral;water quality parameter;Hyperion;estimation;Dongting Lake
摘要：
Water quality parameter is an important indicator to measure the productivity and eutrophication of water. The paper focuses on the estimation research of water quality parameters in Dongting Lake, based on Hyperion image which was acquired on December 28, 2010. Based on atmosphere correction and spectral features analysis, the sensitive bands and band combinations were compared with in situ spectral data. Then estimation model for chlorophyll a concentration and suspended solids concentration were built respectively, and corresponding spatial distribution features were analyzed. Results showed that the effect of Hyperion atmosphere correction was perfect, which can be used for the estimation of water quality parameters. The ratio of band 701 nm and 752nm was suitable for the inversion of chlorophyll a concentration. Mapping results of water quality parameters are reasonable and consistent with the natural distribution rules. The research reveals the potential of Hyperion image on the inland water monitoring, which contributes to the accurate estimation of water quality parameter monitoring.
会议论文集名称：
2010 Second Asia-Pacific Conference on Information Processing(2010年第二届亚太地区信息处理国际会议 APCIP 2010)论文集
关键词：
percent tree cover;TM;SMACC;SVM;classification
摘要：
Remotely sensed forest mapping has become an important way to meet the increasing needs for forest-cover-associated data. However, accuracy for such products varies with the condition of forest ecosystem. In this paper, a support vector machine (SVM) classifier combined with autonomous endmember extraction technique was performed to improve the performance of machine learning in satellite land cover classification and percent tree cover mapping. For the study area, Pingnan County, Guangxi Zhuang Autonomous Region, China, that featured as a complex and fragmented subtropical forest habitat, the TM imagery was first processed with SMACC endmember extraction to find spectral endmembers of expected land cover classes. Secondly, the refined endmembers were input into SVM instead of conventional visual selection of training ROIs. The percent tree cover for the county is 53.6%, underestimated by 1.3% when compared with the National Continuous Forest Inventory 2004 statistics, suggesting a fair agreement with ground truth. The approach also shows a robust performance with an overall RMSE of 10.1. (C) 2012 Published by Elsevier B. V. Selection and/or peer review under responsibility of ICMPBE International Committee.
摘要：
Addressing the problem of spectral mixing in remotely sensed forest cover mapping, a linear spectral unmixing approach was employed in the study to assess if sub-pixel method would improve forest cover estimation accuracy in the context of complex subtropical forest ecosystem. After masking out water bodies using Modified Normalized Difference Water Index (MNDWI), the TM imagery of Pingnan County, Guangxi Zhuang Autonomous Region, China, was processed with Minimum Noise Fraction (MNF) Rotation transform and Pixel Purity Index (PPI), thus “pure” spectral endmembers of woody cover, herbaceous vegetation and bare ground were extracted as input into the spectral unmixing algorithm and produced forest map. The forest percentage is 55.7%, overestimated by 0.8% when compared with the National Continuous Forest Inventory 2004 statistics, reporting a fair agreement with ground truth. The approach also shows a better performance than Spectral Angle Mapper (SAM) classification (overall RMSE of 9.2 compared with 10.7 for latter).
期刊：
Proceedings of 2012 International Conference on Measurement, Information and Control, MIC 2012,2012年1:88-91
通讯作者：
Mo, D.
作者机构：
[Li, Jiping; Sun, Hua; Yan, Enping; Lin, Hui; Mo, Dengkui] Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China;[Zhang, Guozhen] Research Center of Jianke Landscape, Hunan Academy of Building Research, Changsha, China
