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《Earth and Environment》 2008-01
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YANG Tao1,2, XUE Yue1, DAI Ta-gen1(1.School of Geosciences and Environmental Engineering,Central South University,Changsha 410083, China;2.Guizhou Nonferrous Metals Geo-exploration Bureau, Guiyang 550005, China)  
The traditional mineralization which was extracted from remote sensing images only by spectrum or texture has many shortcomings. It may lack information or has a great demand for samples. A new method for extracting mineralization from remote sensing images by SVM based on spectrum and texture is presented in this paper. Jidi was selected as the typical research area. Firstly, selecting the training sample from the research area on the basis of spectrum and texture; secondly, finding out the most optimal hyperplane and decision-making function; finally, extracting the alteration information. After analyzing the spot investigation and comparing the data of the known alteration areas in the mineral geology mapping of field, it is proved that the alteration information is nearly in accordance with the known alteration areas, and four important alteration abnormity districts have also been plotted out.
【Fund】: 中国地质调查项目(1212010660601)
【CateGory Index】: P627
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4 YAN Mei-chun, ZHANG You-jing, BAO Yan-song ( Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Graduate School of Chinese Acdemy of Science, Beijing 100006, China; College of Water Resources and Environment, Hohai university , Nanjing 210098,China; Center of Remote Sensing and GIS ,Beijing Normal University, Beijing 100875, China);Extraction of urban grassland information from IDONOS image based on grey level co-coccurrence matrix[J];测绘工程;2005-01
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5 MIAO Hongmei;;Research on Landscape Pattern of the Western Jilin Province Based on the Object-oriented Technology[J];地理空间信息;2017-11
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【Secondary Citations】
Chinese Journal Full-text Database 10 Hits
1 HUANG Hui ping, WU Bing fang, LI Miao miao, ZHOU Wei feng, WANG Zhong wu (Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101,China);Detecting Urban Vegetation Efficiently with High Resolution Remote Sensing Data[J];遥感学报;2004-01
2 ZHAO Shu-he, FENG Xue-zhi, DU Jin-kang, LIN Guang-fa (Department.of Urban & Resources Sciences, Nanjing University, Nanjing 210093, China);SPIN-2 Panchromatic and SPOT-4 Multi-Spectral Image Fusion Based on Support Vector Machine[J];遥感学报;2003-05
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5 ZHOU Wen\|zuo\+\{1,2\}, PAN Jian\|jun\+1, LIU Gao\|huan\+2 (1. College of Resource and Environmental Sciences, Nanjing Agric Univ, Nanjing 210095, China; 2. Lab of Resource and Environmental Information System, Chinese Academy of Sciences, Beijing 1001;Analysis of Ecological Vegetation in Nanjing City[J];遥感技术与应用;2002-01
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9 LUO Jian_cheng\+1, ZHOU Cheng_hu\+1, LEUNG Yee\+2, MA Jiang_hong\+3 (1. LREIS, Institute of Geographical Science and Natural Resources Research, CAS, Beijing\ 100101, China; 2. Department of Geography, the Chinese University of Hong Kong, Hong Kong, Chi;Support Vector Machine for Spatial Feature Extraction andClassification of Remotely Sensed Imagery[J];遥感学报;2002-01
10 ZHAO Geng xing 1, DOU Yi xiang 2, TIAN Wen xin 3, ZHANG Yin hui 1 (1.Shandong Agricultural University, Tai′an, Shandong 271018; Land Administration Bureau of Shandong Province, Jinan, Shandong 250014;2.Remote Sensing Center of Geological R;Study on Automatic Abstraction Methods of Cultivated Land Information from Satellite Remote Sensing Images[J];地理科学;2001-03
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