Journal of Jilin University(Earth Science Edition) ›› 2020, Vol. 50 ›› Issue (1): 304-312.doi: 10.13278/j.cnki.jjuese.20190024

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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)

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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

CLC Number: 

  • 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.
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