基于OIF和最优尺度分割的GF-2影像分类适用性研究
Research on applicability of GF-2 images classification based on OIF and optimal scale segmentation
摘要
Abstract
GF-2 is a representative of high-resolution remote sensing satellites of China,whose image data′s function to im-prove the quality and accuracy of ground object information extraction is worthy of research and exploration. Optimal waveband combinations are selected by analyzing the characteristics of GF-2′s multispectral data and using the OIF indexes. The ESP opti-mal scale analysis algorithm is selected to obtain the optimal segmentation scales in the research area. On the basis of optimal waveband combinations and optimal segmentation scales,the typical ground objects are extracted and the accuracy of classifica-tion results is verified. The research results show that the optimal waveband combination of GF-2′s multispectral data is 134;the overall classification accuracy of ground object information extracted by means of optimal waveband combinations and opti-mal segmentation scales is larger than 85%,the Kappa coefficient is larger than 0.8,and the accuracy of classification results is high;on the whole,when the selected optimal segmentation scale is 82,the accuracy of the classification results is the highest (the overall accuracy is 93% and the Kappa coefficient is 0.910);when the optimal segmentation scale is 31,the accuracy of the classification results comes to the second(the overall accuracy is 89% and the Kappa coefficient is 0.859);when the opti-mal segmentation scale is 42,the accuracy of the classification results is the lowest(the overall accuracy is 85% and the Kappa coefficient is 0.808).关键词
高分二号/OIF/多尺度分割/面向对象分类/KNN/遥感卫星/地物信息提取Key words
GF-2/OIF/multi-scale segmentation/object-oriented classification/KNN/remote sensing satellite/ground object information extraction分类
信息技术与安全科学引用本文复制引用
任金铜,杨武年,邓晓宇,王蕾,王芳..基于OIF和最优尺度分割的GF-2影像分类适用性研究[J].现代电子技术,2018,41(8):72-77,82,7.基金项目
国家自然科学基金资助项目(41372340) （41372340）
国家自然科学基金(41671432) （41671432）
四川省国土资源厅应用基础研究项目(KJ-2016-12) （KJ-2016-12）
贵州省教育厅自然科学研究项目(黔教合KY字(2015)448号) （黔教合KY字(2015）
贵州省科技厅联合基金项目(黔科合J字LKB[2012]20号、21号) （黔科合J字LKB[2012]20号、21号）
四川省教育厅科研项目重点项目(17ZA0027) Project Supported by National Natural Science Foundation of China(41372340),National Natural Science Foundation of China(41671432),Applied Basic Research Project of Land and Resources Department of Sichuan Province(KJ-2016-12),Natural Science Research Project of Guizhou Provincial Education Department(Qian Jiao He KY(2015)448),Joint Fund Project of Guizhou Provincial Science and Technology Department(Qian Ke He J LKB[2012]20,21),Key Project of Sichuan Provincial Education Department for Scientific Research(17ZA0027) （17ZA0027）
