Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Journal of Central South University of Forestry & Technology》 2015-11
Add to Favorite Get Latest Update

GF-1 analysis of remote sensing image change detection algorithm based on wetland

LIU Wei-le;LIN Hui;SUN Hua;Research Center of Forest Remote Sensing & Information Engineering, Central South University of Forestry & Technology;  
Remote sensing change information detection and recognition has been a technical difficulty of remote sensing dynamic monitoring. This paper takes the east of Dongting Lake as the study area, GF-1 remote sensing image as the research object, on the basis of data pretreatment, divided study area into reed, sedge, Polygonum hydropiperand mud Artemisia, water, mud with 6 types. This study introduce of NDVI vegetation index and the first principal component band(PC1) to improve the traditional image difference algorithm and extract the change information of two images to compare with detection algorithm of support vector machine classification of multitemporal images. The results showed that:(1)Remote sensing images after atmospheric correction and image registration processing, optimum band combination of GF-1 remote sensing image change detection is RGB=432;(2) Using support vector machine classifier to classify the remote sensing image, sample selection degree of isolation were between 1.9-2.0 The overall accuracy of classification results is 85.34% and Kappa coefficient is 0.8 that meet the post classification comparison algorithm to extract change information requirements; Introduction of NDVI and the first principal component to distinguish the change information, using the histogram accumulation interval to confirm change threshold,the result shows that the optimization result is the best when change threshold of increase information is set to 0.3, change threshold of decrease information is set to 0.2, the Smooth Kernel Size is set to 3 and aggregation Min Size is set to 30. Extraction the change information of wetland from remote sensing images of GF-1, compared with the improved image difference algorithm after classification, the image difference method can quickly, directly extract change information and the result is not affected by classification accuracy and sample consistency. The detection precision is 89.6%.Kappa coefficient is 0.9. Image difference algorithm is superior to the traditional classification algorithm and it is an efficient and feasible method.
【Fund】: 国家重大专项(21-Y30B05-9001-13/15-2);; 国家自然科学基金项目(31370639);; 湖南省高校产业化培育项目(13CY011)
【CateGory Index】: P237
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
Chinese Journal Full-text Database 10 Hits
1 ZHU Chang-ming;LI Jun-li;ZHANG Xin;LUO Jian-cheng;Department of Geography and Environment, Jiangsu Normal University;Institute of Remote Sensing Applications, CAS;Xinjiang Institute of Ecology and Geography, CAS;;Bosten Water Resource Dynamic Detection and Feature Analysis in Recent 40 Years by Remote Sensing[J];自然资源学报;2015-01
2 XIE Jing;WANG Zongming;REN Chunying;Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences;Remote Sensing Laboratories,Department of Geography University of Zurich-Irchel;;Analysis of seasonal changes of wetland landscape patterns derived from remote sensing data[J];生态学报;2014-24
3 ZHANG Zhengjian;LI Ainong;LEI Guangbin;BIAN Jinhu;WU Bingfang;Institute of Mountaion Hazards and Environment,Chinese Academy of Sciences;University of Chinese Academy of Sciences;Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences;;Change detection of remote sensing images based on multiscale segmentation and decision tree algorithm over mountainous area: a case study in Panxi region,Sichuan Province[J];生态学报;2014-24
4 HU Yanxia;HUANG Jinliang;DU Yun;HAN Pengpeng;WANG Jiuling;HUANG Wei;Institute of Geodesy and Geophysics,Chinese Academy of Sciences;University of Chinese Academy of Sciences;;Research on methods for extracting change information of the fast-growing poplar in Dongting Lake[J];生态学报;2014-24
5 GAO Yuan-yun;WEN Xiao-rong;LIN Guo-zhong;SHE Guang-hui;WANG Kai;College of Forestry , Nanjing Forestry University;;Extraction of changing information of forest resource monitoring with ZY-1-02C images[J];中南林业科技大学学报;2014-12
6 WU Yiquan;CAO Zhaoqing;TAO Feixiang;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics;Key Laboratory of Agricultural Information Technology,Ministry of Agriculture;Jiangxi Province Key Lab for Digital Land,East China Institute of Technology;;Change detection of remote sensing images by multi-scale geometric analysis and KICA[J];遥感学报;2015-01
7 WU Tong;Colloge of Urban and Environmental Sciences,Liaoning Normal University;;Method and Practice of Change Detection from TM Images Based on ENVI——Change Detection of Part Costal Zone in Dalian[J];经济研究导刊;2014-33
8 LUO Fang-quan;MA Ke-wei;WU Jian-liang;Jiangsu Geologic Surveying and Mapping Institute;;Study on the Method of Remote Sensing Image Change Detection Time[J];现代测绘;2014-06
9 DONG Qi-liang;LIN Hui;SUN Hua;ZANG Zhuo;HU Jia;FAN Ying-long;Research Center of Forestry Remote Sensing & Information Engineering,Central South University of Forestry and Technology;Research Institute of Forest Resources Information Techniques,Chinese Academy of Forestry;;Comparison of Accuracy on Wetland Remote Sensing Classification between Independent Component Analysis and Principal Component Analysis Methods——A Case Study of Wetlands in Western Dongting Lake[J];湿地科学;2014-03
10 CHEN Li;LIN Hui;SUN Hua;Research Center of Forestry Remote Sensing &Information Engineering,Central South University of Forestry & Technology;;WorldView-2Images Based Urban Green Space Information Extraction[J];西北林学院学报;2014-01
【Secondary Citations】
Chinese Journal Full-text Database 10 Hits
1 NING Liang-liang;ZHANG Xiao-li;College of Forestry, Beijing Forestry University;Forest Cultivation and Protection Key Lab. Co-constructed by Labs by Beijing and CAF, Beijing Forestry University;;A preliminary study on vegetation classifi cation based on texture information of Landsat-8 images[J];中南林业科技大学学报;2014-09
2 JIN Jie;ZHU Hai-yan;LI Zi-xiao;SUN Jian-wei;College of Hydropower,Hebei University of Engineering;Water Conservancy Bureau of Handan;;The Comparison of Several Kinds of Supervised Classification Methods in ENVI Remote Sensing Image Processing[J];水利科技与经济;2014-01
3 Zhu Changming1,2,Li Junli3,Zhang Xin2,Luo Jiancheng2,Shen Zhanfeng2 1.City and Environment College,Jiangsu Normal University,Xuzhou 221116,Jiangsu,China 2.Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China 3.Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China;Wetlands Mapping and Spatio-Temporal Change Analysis: A Case Study on Bosten Basin,Xinjiang[J];吉林大学学报(地球科学版);2013-03
4 LIU Gang,XU Hong-jian,MA Hai-tao,LIU Ying-han(Heilongjiang Provincial Land Surveying & Planning Institute,Harbin 150091,China);Land-Use Change Information Extraction Methods and Application of Land Resources High Resolution System Based on Resource 02C Satellite[J];测绘与空间地理信息;2013-04
5 Zhu Jinxia1 Wang Ke2 (1.Institute of Economic and Social Development,Zhejiang University of Finance and Economics,Hangzhou 310018,China 2.Institute of Remote Sensing and Information System Application,Zhejiang University,Hangzhou 310029,China);Object-oriented Change Detection Method Using Very High Spatial Resolution Imagery[J];农业机械学报;2013-04
6 ZHU Changming 1,2,LUO Jiancheng 1,SHEN Zhanfeng1,LI Junli3 1.Institute of Remote Sense Application Institute,Chinese Academy of Sciences,Beijing 100101,China;2.Graduated University,Chinese Academy of Sciences,Beijing 100049,China;3.UCLA Department of Geography,Los Angeles 90095,USA;River Linear Water Adaptive Auto-extraction on Remote Sensing Image Aided by DEM[J];测绘学报;2013-02
7 LIU Li mei1,ZHAO Jing feng1,ZHANG Jian ping2,PENG Wen fu1,FAN Jing long3,ZHANG Tai xi4(1 Sichuan Normal University,Chengdu 610101,Sichuan,China;2 Xinjiang Bazhou EPA,Korla 841000,Xinjiang,China;3 Xinjiang Ecology & Geography Institute,CAS Urumqi 830011,Xinjiang,China;4 Xinjiang Meteorological Administration,Urumqi 830002,Xinjiang,China);Water balance of Lake Bosten using annual water-budgets method for the past 50 years[J];干旱区地理;2013-01
8 DONG Qi-liang,LIN Hui,SUN Hua,QIU Lin,ZHANG Yu(Research Center of Forestry Remote Sensing & Information Engineering,Central South University of Forestry & Technology,Changsha 410004,Hunan,China);Study on applicability of multi-source remote sensing data fusion method in wetland classification[J];中南林业科技大学学报;2013-01
9 WU Yi-quan1,2,HOU Wen1,WU Shi-hua1(1.College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;2.Science and Technology on Electro-optic Control Laboratory,Luoyang 471009,China);Image de-noising based on complex contourlet transform and nonlinear diffusion[J];电路与系统学报;2012-06
10 XIE Jing 1,2,WANG Zong-ming 1,MAO De-hua 1,2,REN Chun-ying 1,HAN Ji-xing 1 (1.Northeast Institute of Geography and Agricultural Ecology,Chinese Academy of Sciences,Changchun 130102,Jilin,P.R.China;2.University of Chinese Academy of Sciences,Beijing 100049,P.R.China);Remote Sensing Classification of Wetlands Using Object-oriented Method and Multi-season HJ-1 Images——A Case Study in the Sanjiang Plain North of the Wandashan Mountain[J];湿地科学;2012-04
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved