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林业资源管理 ›› 2015›› Issue (4): 104-108.doi: 10.13466/j.cnki.lyzygl.2015.04.018

• 科学研究 • 上一篇    下一篇

基于资源3号卫星的面向对象地类信息提取方法研究

孟雪1,2, 温小荣1,2, 林国忠1,2, 佘光辉1,2   

  1. 1.南京林业大学 南方现代林业协同创新中心,南京 210037;
    2.南京林业大学 林学院,南京 210037
  • 出版日期:2015-08-28 发布日期:2020-12-01
  • 通讯作者: 佘光辉,男,教授,主要研究领域:3S技术与森林资源动态监测。Email:ghshe@njfu.edu.cn
  • 作者简介:孟雪(1991-),女,江苏徐州人,在读硕士,主要研究领域3S技术与森林资源动态监测。Email:mengxue1008@yeah.net
  • 基金资助:
    国家948计划项目(2013-4-63);南京林业大学科技创新基金项目(CX2011-24);江苏高校优势学科建设工程自助项目(PAPD)

Study on Land Use Information Extraction with ZY-3 Based on Object-oriented Information Extraction Technology

MENG Xue1, 2, WEN Xiaorong1, 2, LIN Guozhong1, 2, SHE Guanghui1, 2   

  1. 1.Center of Co-Innovation for Sustainable Forestry in Southern China,Nanjing Forestry University,Nanjing 210037,China;
    2.Forestry College of Nanjing Forestry University,Nanjing 210037,China
  • Online:2015-08-28 Published:2020-12-01

摘要: 面向对象分类方法可以充分利用遥感影像的光谱和空间信息,是一种适合于高分辨遥感影像的分类方法。以2012年资源3号卫星高分辨率遥感影像(ZY-3)为数据源,对基于面向对象与最大似然监督分类的地类信息提取方法进行了对比分析。面向对象分析方法中采用改进后的局部方差法确定并选取不同地类类型的最优分割尺度,并采用多尺度层次的方法提取不同地类类型信息。结果表明根据改进后的局部方差法确定的针叶林、阔叶林、针阔混交林地类类型的最优分割尺度为105;农田地类的最优分割尺度为105,水域、建筑类型的最优分割尺度为65。基于面向对象技术的地类信息提取方法其总体精度达到90.3%,Kappa系数为0.82;最大似然法其总体精度为77.6%,Kappa系数为0.71;基于面向对象方法的总体精度提高了12.7%,Kappa系数提高了11%。表明了基于面向对象分析方法的地类信息提取在国产高分辨率影像上的适用性。同时,论文的研究也为森林资源调查中地类信息的遥感提取进行了有益的尝试。

关键词: 资源3号卫星, 面向对象, 最优分割尺度, 模糊分类, 最大似然, 精度评价

Abstract: Object-oriented classification method which is suitable for the high resolution remote sensing images can make full use of the spectral and spatial information of remote sensing images.In this study,the information extraction method of object-oriented was compared to maximum likelihood method based on 2012 ZY-3 satellite high resolution remote sensing image.The results show that the optimal segmentation scale of coniferous and broad-leaves and coniferous and broad-leaves mixed forest is 105,water and building is 65 according to improved local variance method.In object-oriented image analysis,the optimal segmentation scale of different land types was selected by improved local variance method,the ground features of different types were extracted in multi-scale level.The accuracy of high resolution remote sensing image information extraction based on object-oriented image analysis technology was 90.3%,kappa coefficient is 0.82;the accuracy of high resolution remote sensing image information extraction based on maximum likelihood method was 77.6%,kappa coefficient is 0.71;the overall accuracy of the object-oriented image analysis technology is improved by12.7% and Kappa coefficient increases by 11%.It shows obviously that the object-oriented image analysis technology can be applied to domestic high resolution image information extraction.This paper also attempts to extract land use information by remote sensing images in the investigation of forest resources.

Key words: ZY3 Satellite, object-oriented, optimal segmentation scale, fuzzy classification, maximum likelihood, accuracy assessment

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