DOI: 10.3724/SP.J.1047.2014.00902

Journal of Geo-information Science (地球信息科学学报) 2013/15:6 PP.902-910

Automatic Selection of Optimal Segmentation Scale of High-resolution Remote Sensing Images

With the increasing of spatial resolution of imaging sensors, object-oriented feature information extraction technology is developing rapidly. The advantages of object-based classification over the traditional pixel-based approach are well documented. Image segmentation is a key step to realize the object-oriented classification. The choice of scale parameter is very important and has a great influence on the segmentation effectiveness, but the choice of scale parameter is still decided by the repeated attempts and subjective judgments of operator, which are lacking in stability and reliability. Thus, an objective and unsupervised method is proposed for selecting optimal parameter for image segmentation to ensure best quality results. In this paper, WorldView 2 as data source, a new method based on principal component transform is introduced to choose an optimal parameter for image segmentation. We choose principal component images as the editor of image segmentation and eigenvalues as the weights of heterogeneity f and segmentation global score. Segmentation images, ranging from 20 to 200 scale, step by 10, are created in Definiens Professional 8.7. Then, the global intra-segment and inter-segment heterogeneity indexes are taken into account to identify the optimal segmentation scale (i. e. the highest GS value) by using the cubic spline interpolation function method. After comparison with the results of image segmentation based on traditional three bands, image segmentation effect obtained by principal component transform has obvious advantages. As a result, the method in this paper can effectively avoid the subjectivity of the artificial segmentation, one-sidedness and inefficiency, improve the quality of high-resolution image segmentation. The method also makes a good preprocessing work for later image classification and information extraction.

Key words:object-oriented,image segmentation,optimal segmentation scale selection,principal component transform,Moran's Index,WorldView2

ReleaseDate:2015-04-17 13:34:34

[1] 关元秀, 程晓阳.高分辨率卫星影像处理指南[M].北京:科学出版社, 2008, 1-9.

[2] Blaschke T, Strobl J. What's wrong with pixels? Some recent developments interfacing remote sensing and GIS[J]. GIS-Zeitschrift für Geoinformations Systeme, 2001, 14(6):12-17.

[3] Benz U C, Hofmann P, Willhauck G, et al. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS ready information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3-4):239-258.

[4] Blaschke T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(1):2-16.

[5] Kim M, Madden M, Warner T. Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery.//Blaschke T, Lang S, Hay G. (Eds.). Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications[M]. Heidelberg, Berlin, New York: Springer, 2008:291-307.

[6] Definiens Imaging GmbH. Definiens eCognition Developer 8-User Guide[S]. Munich, Germany, 2009:54-56.

[7] Meinel G and Neubert M. A comparison of segmentation programs for high resolution remote sensing data[J]. International Archives of Photogrammetry and Remote Sensing, 2004(35):1097-1105.

[8] Haralick R, Shapiro L. Image segmentation techniques[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(1):100-132.

[9] 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.

[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.

[11] 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(21):91-94.

[12] 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):473-483.

[13] White Paper: The Benefits of the 8 Spectral Bands of WorldView-2[OL], 2009.

[14] Gonzalez-Audieana M, Saleta J L, Catalan R G, et al. Fusion of multispectral and Panchromatic images using improved HIS and PCA mergers based on wavelet decomposition[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2004, 42(6):1291-1299.

[15] 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.

[16] 牛春盈, 江万寿, 黄先锋, 等.面向对象影像信息提取软件Feature Analyst 和eCognition的分析与比较[J].遥感应用, 2007(2):66-70.

[17] 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.

[18] 刘兆祎, 李鑫慧, 沈润平, 等.高分辨率遥感图像分割的最优尺度选择[J].计算机工程与应用, 2012,

[19] 严红萍, 俞兵.主成分分析在遥感图像处理中的应用[J].资源环境与工程, 2006, 20(2):168-170.

[20] 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.

[21] Chabrier S, Emile B, Rosenberger C, et al. Unsupervised performance evaluation of image segmentation[J]. EURASIP Journal on Applied Signal Processing, 2006, DOI: 10.1155/ASP/2006, 96306.

[22] Espindola G, Camara G, Reis I, et al. Parameter selection for region growing image segmentation algorithms using spatial autocorrelation[J]. International Journal of Remote Sensing, 2006, 27(14):3035-3040.

[23] 施吉林, 刘淑珍, 陈桂芝.计算机数值方法(第三版)[M].北京:高等教育出版社, 2009, 85-100.