Optimal Segmentation Scale Selection and Evaluation for Multi-layer Image Recognition and Classification

  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. GraduateUniversity of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2010-10-20

  Revised date: 2011-05-23

  Online published: 2011-06-15


With the rapid increase of remote sensing image storage, it becomes more critical for the quick and effective information extraction from remote sensing imagery. As a widely-used method, object-based image analysis (OBIA) has been rapidly developed from the beginning of this century, but the automatic procedure for land use mapping is still problematic facing with geographical complexity. Regarding to the complex feature contents in the imagery of costal zones, this paper presents a method of optimal segmentation scale extraction and an object-based multi-layer classification procedure. The proposed approach mainly contains three parts: segmentation, optimal scale generation and multi-level classification. First, we select the high resolution images as the data source, segment the imagery with series of scale parameters. Then choose the appropriate scales with the curve of local variance (LV) variation. Variation in heterogeneity is explored by evaluating LV plotted against the corresponding scale in order to get different types of the landuse/cover with their own extraction scales. Finally, we classify the image with multi-features, including spectral, shape, texture and spatial relationship. This paper selects the coastal area of Zhuhai, Guangdong Province as the experiment zone, the classification results show that overall accuracy and Kappa index of the new method are better than those of the traditional pixel-based classifiers and object-oriented classifiers based on the single-level segmentation.

Cite this article

LI Qin, GAO Xizhang, ZHANG Tao, LIU Kun, GONG Jianming . Optimal Segmentation Scale Selection and Evaluation for Multi-layer Image Recognition and Classification[J]. Journal of Geo-information Science, 2011 , 13(3) : 409 -417 . DOI: 10.3724/SP.J.1047.2011.00409


[1] Lobo A, Chic O, Casterad A. Classification of Mediterranean Crops with Multisensor Data: Per-pixel versus Per-object Statistics and Image Segmentation[J]. International Journal of Remote Sensing, 1996, 17: 2358-2400.

[2] Blaschke T and Hay G J. Object-oriented Image Analysis and Scale-space: Theory and Methods for Modeling and Evaluating Multiscale Landscape Structures[J]. International Archives of Photogrammetry and Remote Sensing, 2000,34(4): 22-29.

[3] Blaschke T. Object Based Image Analysis for Remote Sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65: 2-16.

[4] Ursula C B, Hofmann P, Willhauck G. Multi-resolution, Object-oriented Fuzzy Analysis of Remote Sensing Data for GIS-ready Information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58: 219-239.

[5] 周成虎,骆剑承,明冬萍,等. 高分辨率卫星遥感影像地学计算[M]. 北京:科学出版社, 2009,174-186.

[6] 于欢,张树清,孔博,等. 面向对象遥感影像分类的最优分割尺度选择研究[J]. 中国图象图形学报, 2010, 15(2): 352-360.

[7] 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.

[8] Lucian D, Dirk T, Shaun R L. ESP: A Tool to Estimate Scale Parameter for Multi-resolution Image Segmentation of Remotely Sensed Data[J]. International Journal of Geographical Information Science, 2010, 24(6): 859-871.

[9] Kim M, Madden M, Warner T. Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery . Object-Based Image Analysis. Thomas Blaschke, Stefan Lang and Geoffrey J. Hay, 2008, 291-307.

[10] 黄慧萍. 面向对象影像分析中的尺度问题研究 . 中国科学院遥感应用研究所, 2003,124-126.

[11] 于欢,张树清,孔博,等. 面向对象遥感影像分类的最优分割尺度选择研究[J]. 中国图象图形学报, 2010, 15(2): 352-360.

[12] 陈建裕,潘德炉,毛志华. 高分辨率海岸带遥感影像中简单地物的最优分割尺度问题[J]. 中国科学, 2006, 36(11): 1044-1051.

[13] Hay G J, Castilla G. Geographic Object-Based Image Analysis (GEOBIA): A New Name for a New Discipline . // Blaschke T, Lang S, Hay J G (Eds.). Object-Based Image Analysis.,Springer-Verlag, 2008,75-89.

[14] Woodcock C E, Strahler A H. The Factor of Scale in Remote Sensing[J]. Remote Sensing of Environment, 1987(21): 311-332.

[15] 龚剑明,杨晓梅,张涛,等. 基于遥感多特征组合的冰川及其相关地表类型信息提取[J]. 地球信息科学学报,2009, 11 (6): 765-771.

[16] Burnett C, Blaschke T, Shaun R L. A Multi-scale Segmentation/object Relationship Modeling Methodology for Landscape Analysis[J]. Ecological Modeling, 2003, 168: 233-249.

[17] Lang S, Langanke T. Object-basedMapping and Object-relationship Modeling for Land Use Classes and Habitats[J]. Photogrammetrie, Fernerkundung, Geoinformation, 2006, 1: 5-18.

[18] 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.

[19] 刘旭拢,何春阳,潘耀忠,等. 遥感图像分类精度的点、群样本检验与评估[J]. 遥感学报,2006, 10 (3): 366-373.