吉林大学学报(地球科学版) ›› 2020, Vol. 50 ›› Issue (1): 304-312.doi: 10.13278/j.cnki.jjuese.20190024

• 地球探测与信息技术 • 上一篇    

一种面向对象的最优分割尺度计算模型

白韬1, 杨国东1, 王凤艳1, 刘佳为2   

  1. 1. 吉林大学地球探测科学与技术学院, 长春 130026;
    2. 长春市政工程设计研究院, 长春 130000
  • 收稿日期:2019-02-08 发布日期:2020-02-11
  • 通讯作者: 杨国东(1963-),教授,主要从事遥感、GIS及数字化成图方面的研究,E-mail:18844136963@163.com E-mail:18844136963@163.com
  • 作者简介:白韬(1994-),男,硕士研究生,主要从事遥感和GIS应用研究,E-mail:baitao17@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目（41472243）

Object-Oriented Optimal Segmentation Scale Calculation Model

Bai Tao1, Yang Guodong1, Wang Fengyan1, Liu Jiawei2   

  1. 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
    2. Changchun Municipal Engineering Design&Research Institute, Changchun 130000, China
  • Received:2019-02-08 Published:2020-02-11
  • Supported by:
    Supported by National Natural Science Foundation of China (41472243)

PDF (PC)

187

摘要： 作为信息提取和分类的前提，面向对象的影像分割尺度参数的设置直接影响到提取和分类的精度。本文以GF-2影像数据为例，在已有分割理论和方法的基础上提出一种基于最优分割尺度的计算模型（OS模型）。该模型以主成分分析所得的主成分以及新建的归一化植被指数（normalized vegetation index，NDVI）特征层作为分割参考层，综合考虑均质因子的影响，构建加权尺度评价指数，插值拟合最优分割尺度。构建误差系数（Ec）对模型进行评价，结果表明：OS模型误差系数（Ec=1.15%）小于传统模型（Ec=3.28%），且分割对象更均匀、与实际地物更接近。

关键词: 影像分割, GF-2影像, 面向对象, 最优分割尺度, 主成分分析

Abstract: Object-oriented image segmentation is the premise of information extraction and classification, and its scale parameter setting directly affects the accuracy of extraction and classification. Taking GF-2 image data as an example, this paper presented a new optimal scale model based on the existing segmentation theory and method. By taking the obtained components of the principal component analysis and the newly built NDVI feature layer as the segmentation reference layers, the authors carried out multi-scale segmentation. In consideration with the influence of shape factor and compactness factor comprehensively, the weighted scale assessment index was constructed, and the cubic spline interpolation was used to fit the optimal segmentation scale. Finally the error coefficient (Ec) was proposed to compare the new model with the original model. The results show that the error coefficient of the OS model (Ec=1.15%) is smaller than that of the original model (Ec=3.28%), and the segmentation objects of the OS model are closer to the ground truth. This model provides an objective basis for the setting of scale parameters, avoids the subjectivity of traditional parameter selection, and improves the image segmentation quality.

Key words: image segmentation, GF-2 image, object-oriented, optimal segmentation scale, principal component analysis

中图分类号: 

  • P237
[1] Myint S W, Gober P, Brazel A, et al. Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery[J]. Remote Sensing of Environment, 2011, 115(5):1145-1161.
[2] 杨长保, 丁继红. 面向对象的遥感图像分类方法研究[J]. 吉林大学学报(地球科学版), 2006, 36(4):642-646. Yang Changbao, Ding Jihong. Study of Object-Oriented Based Remote Sensing Image Classification[J]. Journal of Jilin University (Earth Science Edition), 2006, 36(4):642-646.
[3] Johnson B, Xie Z. Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(4):473-483.
[4] Shao Peng, Yang Guodong, Niu Xuefeng, et al. Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation[J]. Sustainability, 2014, 6(8):5300-5310.
[5] Chabrier S, Emile B, Rosenberger C, et al. Unsupervised Performance Evaluation of Image Segmentation[J]. EURASIP Journal on Applied Signal Processing, 2006(1):096306.
[6] 黄慧萍,吴炳方,李苗苗,等.高分辨率影像城市绿地快速提取技术与应用[J].遥感学报, 2004, 8(1):68-74. Huang Huiping, Wu Bingfang, Li Miaomiao,et al. Detecting Urban Vegetation Efficiently with High Resolution Remote Sensing Data[J]. Journal of Remote Sensing, 2004, 8(1):68-74.
[7] Espindola G M, Camara G, Reis I A, et al. Parameter Selection for Region-Growing Image Segmentation Algorithms Using Spatial Autocorrelation[J]. International Journal of Remote Sensing, 2006, 27(14):3035-3040.
[8] 张友静,樊恒通.城市植被尺度鉴别与分类研究[J].地理与地理信息科学,2007, 23(6):54-57. Zhang Youjing, Fan Hengtong. Scale Identification for Urban Vegetation Classification Using High Spatial Resolution Satellite Data[J]. Geography and Geo-Information Science, 2007, 23(6):54-57.
[9] Kim M, Madden M, Warner T. Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery[M]. Berlin Heidelberg:Springer, 2008.
[10] 何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学, 2009, 29(1):106-109. He Min, Zhang Wenjun, Wang Weihong. Optimal Segmentation Scale Model Based on Object-Oriented Analysis Method[J]. Journal of Geodesy and Geodynamics, 2009, 29(1):106-109.
[11] 胡文亮,赵萍,董张玉.一种改进的遥感影像面向对象最优分割尺度计算模型[J].地理与地理信息科学, 2010, 26(6):15-18. Hu Wenliang, Zhao Ping, Dong Zhangyu. An Improved Calculation Model of Object-Oriented for the Optimal Segmentation-Scale of Remote Sensing Image[J]. Geography and Geo-Information Science, 2010, 26(6):15-18.
[12] 殷瑞娟,施润和,李镜尧.一种高分辨率遥感影像的最优分割尺度自动选取方法[J].地球信息科学学报, 2013, 15(6):902-910. Yin Ruijuan, Shi Runhe, Li Jingyao. Automatic Selection of Optimal Segmentation Scale of High-Resolution Remote Sensing Images[J]. Journal of Geo-Information Science, 2013, 15(6):902-910.
[13] 詹国旗,杨国东,王凤艳,等.基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究[J].地球信息科学学报, 2018, 20(10):1520-1528. Zhan Guoqi, Yang Guodong, Wang Fengyan, et al. The Random Forest Classification of Wetland from GF-2 Imagery Based on the Optimized Feature Space[J]. Journal of Geo-Information Science, 2018, 20(10):1520-1528.
[14] Defries R S, Townshend J R G. NDVI-Derived Land Cover Classifications at a Global Scale[J]. International Journal of Remote Sensing, 1994, 15(17):3567-3586.
[15] Saucier A, Muller J. Using Principal Component Analysis to Enhance the Generalized Multifractal Analysis Approach to Textural Segmentation:Theory and Application to Microresistivity Well Logs[J]. Physica A:Statistical Mechanics and Its Applications, 2002, 309(3/4):419-444.
[16] Xiao Pengfeng, Zhang Xueliang, Zhang Hongmin, et al. Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image[J]. Remote Sensing, 2018, 10(11):1813-1832.
[17] Baatz M, Schape A. Multiresolution Segmentation:An Optimization Approach for High Quality Multi-Scale Image Segmentation[J]. Angewandte Geographische Information Sverarbeitung, 2000(12):12-23.
[18] Neubert M, Herold H,Meinel G. Assessing Image Segmentation Quality:Concepts, Methods and Application[M]//Lecture Notes in Geoinformation and Cartography. Heidelberg:Springer, 2008:769-784.
[19] 黄慧萍.面向对象影像分析中的尺度问题研究[D].北京:中国科学院遥感应用研究所, 2003. Huang Huiping. Scale Issues in Object-Oriented Image Analysis[D]. Beijing:Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2003.
[20] 张俊,汪云甲,李妍,等.一种面向对象的高分辨率影像最优分割尺度选择算法[J].科技导报, 2009, 27(21):91-94. Zhang Jun, Wang Yunjia, Li Yan, et al. An Object-Oriented Optimal Scale Choice Method for High Spatial Resolution Remote Sensing Image[J]. Science & Technology Review, 2009, 27(21):91-94.
[21] eCognition Developer 9.2 Reference Book[Z]. Munich:Trimble Germany GmbH, 2016.
[1] 贺金鑫, 姜天, 董永胜, 韩凯旭, 马宁, 熊玥. 基于Landsat 8的辽宁弓长岭区遥感蚀变信息提取[J]. 吉林大学学报(地球科学版), 2019, 49(3): 893-901.
[2] 叶涛, 韦阿娟, 黄志, 赵志平, 肖述光. 基于主成分分析法与Bayes判别法组合应用的火山岩岩性定量识别:以渤海海域中生界为例[J]. 吉林大学学报(地球科学版), 2019, 49(3): 872-879.
[3] 束龙仓, 李姝蕾, 王松, 克热木·阿布都米吉提, 鲁程鹏, 李砚阁, 李伟. 岩溶水源地安全供水的风险评价指标筛选——以娘子关泉水源地为例[J]. 吉林大学学报(地球科学版), 2018, 48(3): 805-814.
[4] 王明常, 张馨月, 张旭晴, 王凤艳, 牛雪峰, 王红. 基于极限学习机的GF-2影像分类[J]. 吉林大学学报(地球科学版), 2018, 48(2): 373-378.
[5] 李寅超, 李建松. 一种面向LUCC的时空数据存储管理模型[J]. 吉林大学学报(地球科学版), 2017, 47(1): 294-304.
[6] 陈盟, 吴勇, 高东东, 常鸣. 广汉市平原区浅层地下水化学演化及其控制因素[J]. 吉林大学学报(地球科学版), 2016, 46(3): 831-843.
[7] 于磊，邱殿明，刘莉，凌峰，李巍，胡广鑫，刘国才. 基于SPCA和AHP联合方法的滦河流域生态环境脆弱性变化规律分析[J]. 吉林大学学报(地球科学版), 2013, 43(5): 1588-1594.
[8] 陈圣波，刘彦丽，杨倩，周超，赵靓. 植被覆盖区卫星高光谱遥感岩性分类[J]. 吉林大学学报(地球科学版), 2012, 42(6): 1959-1965.
[9] 蒋立军, 邢立新, 梁一鸿, 潘军, 梁立恒, 黄竞铖. 融合化探信息的遥感异常提取[J]. J4, 2011, 41(3): 932-936.
[10] 张文, 陈剑平, 秦胜伍, 张晨, 李明, 马建全. 基于主成分分析的FCM法在泥石流分类中的应用[J]. J4, 2010, 40(2): 368-372.
[11] 陈永良, 林楠, 李学斌. 求解大样本核主成分分析模型的Lanczos算法[J]. J4, 2010, 40(1): 222-226.
[12] 辛 馨，杨俊鹏，赵奎涛，胡 克. 双台河口土壤中Mn、Zn和Rb的变化规律及其原因分析[J]. J4, 2007, 37(5): 983-0987.
[13] 杨长保，丁继红. 面向对象的遥感图像分类方法研究[J]. J4, 2006, 36(04): 642-646.
[14] 付 哲，周云轩，刘殿伟，刘万崧. 基于特征的面向对象虚拟GIS数据模型设计与原型系统实现[J]. J4, 2006, 36(04): 647-652.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(地球科学版)》编辑部
地址：长春市西民主大街938号(130026)　电话：+86-431-88502374；13756165364　E-mail: xuebao1956@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发