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《Journal of Central South University of Forestry & Technology》 2015-11
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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
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【Citations】
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