高分辨率遥感影像对象分类方法研究及其城乡规划监测应用
|
摘要
随着遥感技术的发展,更高空间分辨率的航天遥感数据产品不断出现,以及航空遥感技术的日臻成熟,为城乡规划遥感监测提供了有效而适用的数据源。本文以WorldView-Ⅱ卫星遥感数据结合高分辨率的航空遥感数据,针对我国迅速城市化过程中城乡规划与建设的监测问题,开展适合高空间分辨率遥感影像的面向对象特征构建、特征选择和城市地物分类等技术研究,并进一步应用于城乡规划的控制性详细规划监测。主要研究内容与结果如下：
(1)在影像多尺度分割生成对象的基础上,研究了对象特征构建算法,重点研究了城市地物较突出的纹理特征和形状特征构建算法。首先研究了基于半变异函数的纹理计算尺度问题,并进一步研究了基于灰度共生矩阵的对象纹理特征计算算法。针对城市建筑地物形状规整的特点,研究了多种形状特征计算算法。
(2)分割后的高分遥感影像对象有光谱、纹理和形状等多种特征的特点,众多特征参与分类,分类效率会降低,但分类精度也未必能提高。针对面向对象分类有益特征选择问题,研究了对象特征选择的技术流程,首先基于改进的ReliefF去除无关特征,再利用特征间的互信息初步去除冗余特征,形成特征粗集,最后利用遗传算法,以类内、类间距离为适应度函数,对影像对象分类的最优特征子集进行搜索和评价,最终得到有益于城乡地物对象分类的特征子集。
(3)针对面向对象分类的小样本问题,结合SVM适合小样本的特点,本文针对基于SVM的面向对象分类中的部分环节进行了研究。针对径向基核函数SVM的C和γ参数对分类精度影响大的问题,在前人研究的基础上,结合城市监测的需要,研究了三步搜索法的参数优化算法。SVM是针对二分类问题,本文设计了多类分类的流程,即在投票法SVM分类的基础上,进一步利用最近邻法,对票数相同或相差一票的情况进一步分类。最后,以北京市某2个区域为研究区,以高分辨率的WorldView-Ⅱ航天卫星遥感影像为数据源,辅以研究区航空遥感影像,构建遥感城乡土地利用分类体系。在影像分割的基础上,利用本文研究的特征构建算法、特征优选技术流程,以及基于改进SVM分类算法,完成研究区的城市地类分类。最后以研究区的城市规划图为依据,对该区域的城市规划执行情况进行监测和评价。
With the development of the remote sensing (RS) technology, the high spatial resolution satellite remote sensing data are presented and the aerial remote sensing technology has been full developed, which provided good data source for the RS monitoring of the urban and rural plan. So the WorldView-Ⅱ data and the high spatial resolution aerial RS image are used in this paper. To the need of the monitoring and management of urban and rural, the object features computing algorithm and feature selection algorithm and classification based object-oriented (OO) methods for urban and rural land use are studied and used in the urban and rural plan monitoring. The main contents and the results are as following:
Based on the high spatial remote sensing image segmentation algorithm, the feature computeing algorithm are studied. The obvious texture and shape features of the city object are focused on The scale of texture computing are studied based on semivariogram and the object texture computing algorithm are studied basen grey-level co-occurrence matrix. To the characteristic of the city building objects, several shape features computeing are studied in this paper.
There are many features of the object such as spectral features, texture features, shape features and so on. The classification effective will reduce and the classification precision may not be improved with the total features of the objects. To the feature selection of the object-oriented classification, the technology flow of feature selection is formed firstly. The enhanced ReliefF algorithm is adopted to filter the irrelevant features. Then the mutual information among features is computed to eliminate the redundant features. At last, the genetic algorithm is used and the distance between the same classes and the different classes acted as the fitness function.the features set are searched and evaluated, which is beneficial to the city land use classifcication.
To the problem of the small sample size in the object-oriented classification, and the support vector machine (SVM) is fit to the small sample size problem, parts of application SVM in the OO classification are studied in this paper. The classification precision are severely impacted by the C and γ parameters in SVM based radial basis function. Based on the research of the former and combined with the need of the urban and rural plan monitoring and management, the three step search method are used to parameters optimization. The SVM is to the two classes, so the multiple classes flow is designed in this paper. After classification based on the vote, further the Nearest Neighhood (NN) classifier are used to the case of vote equiation and one difference vote.
Two area in Beijing are the study area and the WorldView-Ⅱ data and the aerial RS image with0.2m spatial resolution are used as the main data sources. The classification system of the urban and rural land use with RS data are designed. Based on the image segmentation and the features computeing and features selection algorithm presented in this paper, then the urban and rural land use is classified with the improved SVM classification. At last comparing with the urban and rural plan map of the area, the urban and rural plan are monitored and evaluated.
(1)在影像多尺度分割生成对象的基础上,研究了对象特征构建算法,重点研究了城市地物较突出的纹理特征和形状特征构建算法。首先研究了基于半变异函数的纹理计算尺度问题,并进一步研究了基于灰度共生矩阵的对象纹理特征计算算法。针对城市建筑地物形状规整的特点,研究了多种形状特征计算算法。
(2)分割后的高分遥感影像对象有光谱、纹理和形状等多种特征的特点,众多特征参与分类,分类效率会降低,但分类精度也未必能提高。针对面向对象分类有益特征选择问题,研究了对象特征选择的技术流程,首先基于改进的ReliefF去除无关特征,再利用特征间的互信息初步去除冗余特征,形成特征粗集,最后利用遗传算法,以类内、类间距离为适应度函数,对影像对象分类的最优特征子集进行搜索和评价,最终得到有益于城乡地物对象分类的特征子集。
(3)针对面向对象分类的小样本问题,结合SVM适合小样本的特点,本文针对基于SVM的面向对象分类中的部分环节进行了研究。针对径向基核函数SVM的C和γ参数对分类精度影响大的问题,在前人研究的基础上,结合城市监测的需要,研究了三步搜索法的参数优化算法。SVM是针对二分类问题,本文设计了多类分类的流程,即在投票法SVM分类的基础上,进一步利用最近邻法,对票数相同或相差一票的情况进一步分类。最后,以北京市某2个区域为研究区,以高分辨率的WorldView-Ⅱ航天卫星遥感影像为数据源,辅以研究区航空遥感影像,构建遥感城乡土地利用分类体系。在影像分割的基础上,利用本文研究的特征构建算法、特征优选技术流程,以及基于改进SVM分类算法,完成研究区的城市地类分类。最后以研究区的城市规划图为依据,对该区域的城市规划执行情况进行监测和评价。
With the development of the remote sensing (RS) technology, the high spatial resolution satellite remote sensing data are presented and the aerial remote sensing technology has been full developed, which provided good data source for the RS monitoring of the urban and rural plan. So the WorldView-Ⅱ data and the high spatial resolution aerial RS image are used in this paper. To the need of the monitoring and management of urban and rural, the object features computing algorithm and feature selection algorithm and classification based object-oriented (OO) methods for urban and rural land use are studied and used in the urban and rural plan monitoring. The main contents and the results are as following:
Based on the high spatial remote sensing image segmentation algorithm, the feature computeing algorithm are studied. The obvious texture and shape features of the city object are focused on The scale of texture computing are studied based on semivariogram and the object texture computing algorithm are studied basen grey-level co-occurrence matrix. To the characteristic of the city building objects, several shape features computeing are studied in this paper.
There are many features of the object such as spectral features, texture features, shape features and so on. The classification effective will reduce and the classification precision may not be improved with the total features of the objects. To the feature selection of the object-oriented classification, the technology flow of feature selection is formed firstly. The enhanced ReliefF algorithm is adopted to filter the irrelevant features. Then the mutual information among features is computed to eliminate the redundant features. At last, the genetic algorithm is used and the distance between the same classes and the different classes acted as the fitness function.the features set are searched and evaluated, which is beneficial to the city land use classifcication.
To the problem of the small sample size in the object-oriented classification, and the support vector machine (SVM) is fit to the small sample size problem, parts of application SVM in the OO classification are studied in this paper. The classification precision are severely impacted by the C and γ parameters in SVM based radial basis function. Based on the research of the former and combined with the need of the urban and rural plan monitoring and management, the three step search method are used to parameters optimization. The SVM is to the two classes, so the multiple classes flow is designed in this paper. After classification based on the vote, further the Nearest Neighhood (NN) classifier are used to the case of vote equiation and one difference vote.
Two area in Beijing are the study area and the WorldView-Ⅱ data and the aerial RS image with0.2m spatial resolution are used as the main data sources. The classification system of the urban and rural land use with RS data are designed. Based on the image segmentation and the features computeing and features selection algorithm presented in this paper, then the urban and rural land use is classified with the improved SVM classification. At last comparing with the urban and rural plan map of the area, the urban and rural plan are monitored and evaluated.
引文
[1]AG, D. Definiens Professional 7 User Guide. Munchen, Germany:Definiens AG,2007.
[2]Canny John. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[3]Chapelle, O., Vapnik, V., Bousquet, O. et al. Choosing multiple parameters for support vector machines. Machine Learning.2002,46(1-3):131-159.
[4]Clausi, D. A., Zhao, Y. Rapid extraction of image texture by co-occurrence using a hybrid data structure. Computers and Geosciences.2002,28(6):763-774.
[5]D Koller, M Sahami. Toward Optimal Feature Selection. Proc of Int'l Conf on Machine Learning.1996.284-292.
[6]Dash J., Steinle E., Singh R.P. et al. Automatic Building Extraction from Laser Scanning Data:an Input Tool for Disaster Management. Advances in Space Research,2004,33:317-322.
[7]Davies S,Russl S.Np-completeness of searches for smallest possible feature sets.Proceedings of the AAAI Fall 94 Symposium on Relevance. Menlo Park,CA.:AAAI Press,1994,37-39
[8]de Diego, I. M., Serrano, A., Conde, C. et al. Face verification with a kernel fusion method. Pattern Recognition Letters.2010.
[9]Duda R E, Hart P E. Pattern Classification and Scene Analysis[M]. New York,Wiley,1973
[10]Duda, R. O., Hart, P. E., Stork, D. G.李宏东,姚天翔译.模式分类(第二版).北京：机械工业出版社,2003.
[11]Foody, G. M., Mathur, A. Toward intelligent training of supervised image classifications:Directing training data acquisition for SVM classification. Remote Sensing of Environment.2004,93(1-2): 107-117.
[12]Frank A, van den Heuvel. Estimation of Interior Orientation Parameters from Constraints on Line [3] Measurements in a Single Image. International Archives of Photogrammetry and Remote Sensing,1999,32(5):81-88
[13]G H John, R Kohavi, K Pfleger. Irrelevant Features and the Subset Selection Problem. Proc of the 11th Int'l Conf on Machine Learning.1994.121-129
[14]Geneletti D., Gorte B. G. H., A Method For Object-Oriented Land Cover Classification Combining Landsat TM Data and Aerial Photographs. International Journal of Remote Sensing, 2003,24 (6):1273-1286.
[15]Gitas, I. Z., Polychronaki, A., Katagis, T. et al. Contribution of remote sensing to disaster management activities:A case study of the large fires in the Peloponnese, Greece. International Journal of Remote Sensing.2008,29(6):1847-1853.
[16]Greunaw. Adaptive Least Square Correlation:A Powerful Image Matching Technique.S Afr J of Photogrammetry, RS and Cartography,1985,14(3):175-187
[17]Grigorescu C., Petkov N., Westenberg M. A.. Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing.2003,12(7):729-739
[18]Haala N., Brenner C. Extraction of Buildings and Trees in Urban Environments. ISPRS Journal of Photogrammetry and Remote Sensing,1999,54(2-3):130-137.
[19]hackelford A. K. and Davis C. H. A. Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multispectral Data over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing,2003,41, (10):2354-2363.
[20]Haralick, R. M, Shanmugam, K., Dinstein, I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics.1973, smc 3(6):610-621.
[21]Hellwich O., Wiedemann C. Object Extraction from High-Resolution Multi-Sensor Image Data. The 3rd International Conference on Fusion of Earth Data. Sophia Antipolis, France, SEEGreCA, Nice,2000.105-115.
[22]Hofmann P. Detecting informal settlements from IKONOS image data using methods of object oriented image analysis-an example from Cape Town (South Africa), In:Remote Sensing of Urban Areas, edited by Jurgens, Carsten (Regensburg),2001,41-52.
[23]Hsu, C. W., Lin, C. J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks.2002,13(2):415-425.
[24]Hu, H., Ban, Y. Urban landuse/land-cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis. In:Proceedings of SPIE-The International Society for Optical Engineering.2008.
[25]Hughes, G. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory.1968,14(1):55-63.
[26]K Kira, LA Rendell. The Feature Selection Problem:Traditional Methods and a New Algorithm. Proc of 9th National Conf on AI.1992.129-134
[27]Kass M., Witkin A., Terzopoulos D.. Snakes:Active contour models. International Journal of Computer Vision,1988,1 (4):321-331
[28]Kettig R. L., Landgrebe D. A. Computer Classification of Remotely Sensed Multispectral Image Data Extraction and Classification of Homogeneous Objects, IEEE Transactions on Geoscience Electronics,1976,14(1):19-26.
[29]Kim,T. A study on the epipolarity of linear pushbroon images. Photogrammetric Engineering and Remote Sensing,2000,66(8):961-966
[30]Koga, T., Iinuma, K., Hirano, A. et al. Motion compensated interframe coding for video conferencing. In:Proc. Nat. Telecommun. Conf.1981.1-5.
[31]Kononenko I. Estimation attributes:analysis and extensions of RELIEF. Proceedings of the 1994 European Conference on Machine Learning,1994,171-182
[32]Lewis P M,The characteristic selection problem in recognition system.IRE Transaction on Information Theory,1962.8:171-178
[33]Li, H. T., Gu, H. Y., Han, Y. S. et al. Object-oriented classification of polarimetric SAR imagery based on statistical region merging and support vector machine. In:2008 International Workshop on Earth Observation and Remote Sensing Applications, EORSA.2008.
[34]Manoranjan Dash,Huan Liu. Feature Selection for Classification. Intelligent Data Analysis,1997, 1(3):131-156
[35]Mariz, C., Gianelle, D., Bruzzone, L. et al. Fusion of multi-spectral SPOT-5 images and very high resolution texture information extracted from digital orthophotos for automatic classification of complex Alpine areas. International Journal of Remote Sensing.2009,30(11):2859-2873.
[36]Marko Robnik-Sikonja, Igor Kononenko. Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal,2003,53:23-69
[37]Navia-Vazquez, A. Compact multi-class support vector machine. Neurocomputing.2007,71(1-3): 400-405.
[38]Nelson, J. D. B., Damper, R. I., Gunn, S. R. et al. A signal theory approach to support vector classification:The sinc kernel. Neural Networks.2009,22(1):49-57.
[39]Nichol J. and Lee. C. M. Urban Vegetation Monitoring in Hong Kong Using High Resolution Multispectral Images. International Journal of Remote Sensing,2005,26(10):903-918.
[40]Oladunni, O. O., Trafalis, T. B., Papavassiliou, D. V. Knowledge-based multiclass support vector machines applied to vertical two-phase flow. In:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2006:188-195.
[41]Opper, M., Winther, O. Gaussian processes and SVM:Mean field and leave-one-out. Advances in Large Margin Classifiers.2000:311-326.
[42]Otto GP,Chau T K. A Region-Growing Algorithm for Matching of Terrain Images. Image and Vision Computing,1989,7(2):83-94
[43]Pal, M., Mather, P. M. Support vector machines for classification in remote sensing. International Journal of Remote Sensing.2005,26(5):1007-1011.
[44]Perez-Cruz, F., Bousquet, O. Kernel methods and their potential use in signal processing. IEEE Signal Processing Magazine.2004,21(3):57-65.
[45]Platt, J., Cristianini, N., Shawe-Taylor, J. Large margin DAGs for multiclass classification. Advances in neural information processing systems.2000,12(3):547-553.
[46]R.Jensen(著)陈晓玲等(译).遥感数字影像处理导论.北京：机械工业出版社,2007.
[47]Rottensteiner F., Trinder J., Clode S. et al. Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images. Image and Vision Computing,2003,60-65.
[48]Ruvimbo G., Philippe D., Morgan D.. Object-oriented change detection for the city of Harare, Zimbabwe. Zimbabwe, Expert Systems with Applications (2007), doi:10.1016/j.eswa.2007.09.067
[49]Scholkopf, B., Smola, A. J., Williamson, R. C. et al. New support vector algorithms. Neural Computation.2000,12(5):1207-1245.
[50]Shahidi, R., Moloney, C. Decorrelating the structure and texture components of a variational decomposition model. IEEE Transactions on Image Processing.2009,18(2):299-309.
[51]Shevade, S. K., Keerthi, S. S., Bhattacharyya, C. et al. Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks.2000,11(5):1188-1193.
[52]Singh, M, Singh, S. Spatial Texture Analysis:A Comparative Study. In:Pattern Recognition,2002. Proceedings.16th International Conference on Pattern Recognition.2002.676-679.
[53]Smith, G. M., Fuller, R. M. An integrated approach to land cover classification:An example in the Island of Jersey. International Journal of Remote Sensing.2001,22(16):3123-3142.
[54]Sohel S., Dare P., Jones S., Automatic Classification of Land Cover Features with High Resolution Imagery and LIDAR Data:an Object Oriented Approach.2005, Proceedings of SSC2005 Spatial Intelligence, Innovation and Praxis:The national biennial Conference of the Spatial Sciences Institute.
[55]T M. Cover. The best two independent measurements are not the two best.IEEE Trans. Syst.Man Cybern,1974,4 (2):116-117
[56]T.E.Chen,R.Shibasaki.3D modeling and visualization of building in area by linear, photogrammetry. Proceeding of UM3, Japan,1998
[57]T.Toussaint. Note on optimal selection of independent binary-valued features for pattern. IEEE: Transactions on information theory.1997,618.
[58]Tarabalka, Y., Chanussot, J., Benediktsson, J. A. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition.2010.
[59]Tatsumi, K., Hayashida, K., Higashi, H. et al. Multi-objective multiclass support vector machine for pattern recognition. In:Proceedings of the SICE Annual Conference.2007.1095-1098.
[60]Tax, D., Duin, R. Support vector domain description. Pattern Recognition Letters.1999,20(11-13): 1191-1199.
[61]Tosun, A. B., Kandemir, M., Sokmensuer, C. et al. Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition.2009,42(6): 1104-1112.
[62]Tupin F, Roux M.3D information extraction by structural Matching of SAR and optical features. http://www.isprs.org/istanbul/comml/papers/88.pdf:ISPRS,2004
[63]Tupin F, Roux M. Detection of building outlines based on the fusion of SAR and optical features. ISPRS Journel of Photogrammetry & Remote Sensing,2003,58:71.
[64]Tupin F. Merging of SAR and optical features for 3D reconstruction in a radargrammetric framework. In:International Geoscience and Remote Sensing Symposium, Alaska,2004,126-129
[65]Vapnik, V. N. An overview of statistical learning theory. In:IEEE Transactions on Neural Networks; 1999:988-999.
[66]Vapnik, V. N. Statistical Learning Theory. New York:John Wiley & Sons,1998.
[67]Vapnik, V. N.,张学工译.统计学习理论的本质.北京：清华大学出版社,2000.
[68]Vapnik, V., Chapelle, O. Bounds on error expectation for support vector machines. Neural Computation.2000,12(9):2013-2036.
[69]Vapnik. V., Chervonenkis, A. On uniform convergence of the frequencies of events to their probabilities. Teoriya Veroyatnostei i ee Primeneniya.1971,16(2):264-279.
[70]Walter, V. Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing.2004,58(3-4):225-238.
[71]Wang L. Integration of Object-Based and Pixel-Based Classification for Mapping Mangroves with IKONOS Imagery. International Journal of Remote Sensing,2004,25(24):5655-5668.
[72]Wang, L., Sousa, W. P., Gong, P. et al. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment.2004, 91(3-4):432-440.
[73]Weidner U and Forstner W. Towards automatic building extraction from high resolution digital elevation models. ISPRS Journal,1995,50(4)
[74]Wesaka, J. S., Rosenfeld, A., Dyer, C. R. A Comparative Study of Texture Measures for Terrain Classification. IEEE Trans. Syst., Man. & Cybern.1976,6:269-285.
[75]Wharton S. A. Spectral-Knowledge-Based Approach for Urban and Land-cover Discrimination, IEEE Transactions on Geoscience and Remote Sensing,1987,25(3):272-282.
[76]Wu, K. P., Wang, S. D. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognition.2009,42(5):710-717
[77]Xie, Z., Roberts, C., Johnson, B. Object-based target search using remotely sensed data:A case study in detecting invasive exotic Australian Pine in south Florida. ISPRS Journal of Photogrammetry and Remote Sensing.2008,63(6):647-660.
[78]Zaakkola, T. S., Haussler, D. Probabilistic kernel regression models. In:Proceedings of the 1999 Conference on AI and Statistics.1999.
[79]Zhan Q. M., Molenaar M., Tempfli K. Hierarchical Image-Object based Structural Analysis toward Urban Land Use Classification using High Resolution Imagery and Airborne LIDAR Data Proceedings of the 3rd International Symposium on Remote Sensing of Urban Areas,2002.
[80]薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析.电子学报.2006,34(1)：155-158,134.
[81]蔡涛,王润生.一个从多波段遥感图像提取道路网的算法.软件学报,2001,12(6)：943-948
[82]曹淑娟.核空间中的平分最近点法与模糊支持向量机：[硕士学位论文].武汉：华中科技大学,2006.
[83]曹雪,柯长青.基于对象级的高分辨率遥感影像分类研究.遥感信息.2006,(5)：27-30,51.
[84]陈金丽.面向对象的最邻近算法研究与实现：[硕士学位论文].北京：中国地质大学(北京),2009.
[85]陈亮,张友静,陈波.结合多尺度纹理的高分辨率遥感影像决策树分类.地理与地理信息科学.2007,23(4)：18-21.
[86]陈敏捷.基于支持向量机的面向对象高分辨率遥感影像分类方法研究：[硕士学位论文].北京：中国农业大学,2010.
[87]陈启浩,高伟,刘修国.辅以纹理特征的高分辨率遥感影像分类.测绘科学.2008,33(1)：88-90.
[88]陈庆华,王雷QuickBird影像在城市规划监察中的应用.交通科技与经济,2007,9(6)：61-62
[89]陈书海,傅录祥.实用数字图像处理.科学出版社,2005
[90]陈述彭,谢传节.城市遥感与城市信息系统.测绘科学.2000,25(1)：1-9.
[91]陈云浩,冯通,史培军.基于面向对象和规则的遥感影像分类研究.武汉大学学报：信息科学版.2006,31(4)：316-320.
[92]陈忠.高分辨率遥感图像分类技术研究：[博士学位论文].北京：中国科学院研究生院(遥感应用研究所),2006.
[93]崔丽娜,龚威平,郝惠.利用卫星遥感技术辅助城乡规划督察成果应用研究—以2008～2011年郑州市动态监测成果为例.Urban Studies,2012,19(6):10-14.
[94]党安荣.遥感技术在城市规划研究中的应用.小城镇建设,2001,7：22-23
[95]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004.
[96]邓乃扬,田英杰.支持向量机--理论、算法与拓展.北京：科学出版社,2009.
[97]杜凤兰,田庆久,夏学齐.面向对象的地物分类法分析与评价.遥感技术与应用.2004,19(1)：20-23.
[98]冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究.北京测绘.2007,(3)：19-22.
[99]付静.面向旋转机械的支持向量机故障智能诊断方法及实验研究：[硕士论文].赣州市：江西理工大学,2008.
[100]高瑞莲,邹凤,周海波.多源遥感数据融合在城市发展研究中的应用.信阳师范学院学报(自然科学版),2001,01：114-117
[101]宫鹏,黎夏,徐冰.高分辨率影像解译理论与应用方法中的一些研究问题.遥感学报,2006,10(1)：1-5
[102]巩丹超,张永生.基于航空影像的建筑物半自动提取技术研究.测绘通报,2002,10：15-18
[103]郭春燕.基于支持向量机的高光谱遥感图像分类：[硕士论文].哈尔滨：哈尔滨工程大学,2007.
[104]郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究.系统仿真学报.2006,18(07)：2033-2036+2051.
[105]郭雷,肖怀铁,付强.基于支持矢量机特征空间距离的多目标分类方法.计算机仿真.2005,(09)：272-274.
[106]贺江兵.城区建设中合理的城市规划策略,科技创新导报报,2011,21：112.
[107]侯蕾,尹东,尤晓建.一种遥感图像中建筑物的自动提取方法.计算机仿真,2006,4(23)：184-187
[108]胡慧萍.面向对象分类技术的景观信息获取：[硕士学位论文].长沙：中南大学,2007.
[109]胡亚明,王明厉.应用遥感技术实现城市规划动态监察.城市勘测,2005,2：22-24
[110]黄慧萍,吴炳方,李苗苗.高分辨率影像城市绿地快速提取技术与应用.遥感学报,2004,8(1)：68-69.
[111]黄慧萍,吴炳方.地物大小、对象尺度、影像分辨率的关系分析.遥感技术与应用.2006,21(3)：243-248.
[112]黄景涛,马龙华,钱积新.基于统计试验设计方法的支持向量机参数选取.电路与系统学报.2008,(06).
[113]黄景涛.支持向量机算法参数选择及其在电站锅炉系统中的应用研究：[博士学位论文].浙江杭州：浙江大学,2005.
[114]惠文华.基于支持向量机的遥感图像分类方法.地球科学与环境学报.2006,28(2)：93-95.
[115]姜波,陈庆华,于宁.遥感技术在哈尔滨市数字城市规划中的应用.交通科技与经济,2007,2：76-77；79
[116]孔春芳,吴冲龙,徐凯.基于多特征的城市用地高分辨率影像分类与提取.数据采集与处理报,2007,22(3)：315-320.
[117]李成范.城市土地利用变化的遥感监测：[硕士学位论文].重庆：西南大学,2009.
[118]李德仁.论21世纪遥感与GIS的发展.武汉大学学报(信息科学版).2003,(02)：127-131.
[119]李蓉,叶世伟,史忠植.SVM-KNN分类器——一种提高SVM分类精度的新方法.电子学报.2002,(05)：745-748.
[120]李坤,徐维朴,郑南宁,等.一种新的基于遗传算法的快速运动估计方法.电子学报.2000,(06)：114-117.
[121]梁松.城市规划动态监管卫星遥感关键技术研究：[博士学位论文].北京：中国矿业大学,2009.
[122]廖圣东,廖其芳,曾文华,等.高分辨率遥感影像在城市二维景观建立中的应用.国土资源遥感,2006,4(70)：40-42.
[123]林升梁,刘志.基于RBF核函数的支持向量机参数选择.浙江工业大学学报.2007,35(02)：163-167
[124]林贤金.基于信息理论的入侵检测技术研究：[硕士论文].福州：福州大学,2002.
[125]刘建贵,张兵,郑兰芬,等.基于光谱特征的城市人工地物分级分类方法研究.遥感技术与应用,1999,14(3)：1-6.
[126]刘龙飞,陈云浩,李京.遥感影像纹理分析方法综述与展望.遥感技术与应用.2003,18(6)：441-447.
[127]刘志刚,史文中,李德仁.一种基于支撑向量机的遥感影像不完全监督分类新方法.遥感学报.2005,9(4)：363-373.
[128]刘志刚.支撑向量机在光谱遥感影像分类中的若干问题研究：[博士学位论文].湖北武汉：武汉大学,2004.
[129]陆景辉.基于信息理论的特征选择算法研究：[硕士论文].北京：北京交通大学,2007.
[130]罗名海.3S技术的发展趋势与在城市规划中的应用前景.地理空间信息,2004,02(4)：4-7
[131]马晶,余洁,张继贤,等.影像融合技术在土地利用动态遥感监测中的应用研究.测绘科学,2007,32(2)：128-130.
[132]马廷.高分辨率卫星影像及其信息处理的技术模型.遥感信息.2001,(03)：6-10.
[133]马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用.控制与决策.2003,(03)：272-276+284.
[134]蒲智,刘萍,杨辽,葛挺进.面向对象技术在城市绿地信息提取中的应用.福建林业科技,2006,33(1)40-44.
[135]齐志泉,田英杰,徐志洁.支持向量机中的核参数选择问题.控制工程.2005,12(04)：379-381.
[136]钦伟瑾.基于对象的城市遥感影像分类方法及应用研究：[硕士学位论文].西安：长安大学,2009.
[137]曲瀛.硬件演化技术及其应用基础研究：[硕士论文].南京：南京航空航天大学,2002.
[138]荣海娜,张葛祥,金炜东.系统辨识中支持向量机核函数及其参数的研究.系统仿真学报.2006, 18(11)：3204-3208+3226.
[139]沈琪,马金辉.高分辨率遥感数据在现代城市规划中的应用.甘肃科学学报,2006,18(1)：44-48
[140]宋刚贤.基于遥感影像的城市违规建设用地监测研究：[硕士学位论文].南京：南京农业大学,2008.
[141]宋晓宇,单新建.高分辨率卫星影像在城市建筑物识别中的初步应用.遥感信息,2002,1：27-31
[142]苏伟,李京,陈云浩等,基于多尺度影像分割的面向对象城市土地覆被分类研究--以马来西亚基隆坡市城市中心区为例,遥感学报,2007,11(4)：530-539.
[143]孙旭红.遥感系统、地理信息系统在城市规划监测中的应用.科学技术与工程,2006,6(11)：1576-1579；1588.
[144]孙燕,李利军,王乘等.3S支持下违章建筑监管平台架构及关键技术。计算机技术与发展,2006,16(10)：174-176.
[145]唐亮,谢维信,黄建军等.从航空影像中自动提取高层建筑物.计算机学报,2005,7(28)：1199-1204.
[146]汪波,龚威平,孙越,刘树军.卫星遥感数据在城乡规划动态监测中的应用.航天器工程,2011,20(6)：113-117.
[147]汪求来.面向对象遥感影像分类方法及其应用研究：[硕士学位论文].江苏南京：南京林业大学,2008.
[148]王辉.基于核主成分分析特征提取及支持向量机的人脸识别应用研究：[硕士学位论文].安徽合肥：合肥工业大学,2006.
[149]王琳,徐涵秋,李胜.福州城市扩展的遥感动态监测.地球信息科学,2006,4(8)：129-135
[150]王荣静.基于svm的MODIS数据土地覆盖分类方法研究：[硕士学位论文].北京：中国农业大学,2005.
[151]王亚旗.“城中村”改造模式探究：[硕士论文].西安：西安建筑科技大学,2010.
[152]王祖伟,秦其明.多源遥感数据融合及在城市研究中的应用.测绘通报,2002,03：22-24
[153]王佐成.基于纹理的遥感图像分类研究：[博士学位论文].四川成都：西南交通大学,2007.
[154]韦娜,王涛.结合ReliefF与支持向量机的特征选择方法研究.计算机应用与软件.2008,1：283-285.
[155]魏飞鸣.基于对象信息的遥感影像分类研究：[硕士学位论文].成都：电子科技大学,2008.
[156]魏玉璋.基于QuickBird影像的土地利用信息提取技术研究：[硕士学位论文].浙江杭州：浙江大学,2006.
[157]吴海平.用高分辨率IKONOS影像测定城市建筑容积率的研究[硕士学位论文].北京：中国农业大学,2003
[158]吴健平,张立.卫星遥感技术在城市规划中的应用.遥感技术与应用,2003,18(1)：52-56.
[159]吴琳琳.基于SVM技术的手写数字识别的研究：[硕士论文].济南：山东师范大学,2006.
[160]徐芳.航空影像中地物识别的SVM方法研究.测绘通报.2004,(02)：34-36.
[161]徐振华,韦松林,张燕妮等.3S技术的发展趋势及其在城市规划中的应用前景.科技情报开发与经济,2005,15(12)：139-141.
[162]薛晨荣.形状特征在交通类目标识别中的应用：[硕士学位论文].安徽合肥：中国科学技术大学, 2008.
[163]颜根廷,李传江,马广富.基于混合遗传算法的支持向量机参数选择.哈尔滨工业大学学报.2008,40(05)：688-691.
[164]杨惠岚.多源卫星遥感影像在城市规划中的应用.科技情报开发与经济,2006,16(19)：145-146；149.
[165]杨利英,覃征,王卫红.多分类器融合系统设计与应用,计算机工程,2005,31(5)：175-177.
[166]杨宁.高分辨率影像面向对象分类特征选择方法研究：[硕士论文].西安：西安科技大学,2012.
[167]杨希明.高光谱遥感图像分类方法研究：[硕士论文].哈尔滨：哈尔滨工程大学,2007.
[168]殷小舟.一种改进的结合K近邻法的SVM分类算法.中国图象图形学报.2009,(11)：2299-2303.
[169]于静,程鸿.加强城市遥感影像技术在城市规划监控中的应用.城乡建设,2007,03：63-64
[170]袁胜发,褚福磊.SVM多类分类算法及其在故障诊断中的应用.振动工程学报.2004,17(S)：419-421.
[171]张桂峰.粒度理论下的多尺度遥感影像分割：[博士学位论文].武汉：武汉大学,2010.
[172]张建国,李宪文,吴延磊.面向对象的冬小麦种植面积遥感估算研究.农业工程学报.2008,24(5)：156-160.
[173]张锦水,何春阳,潘耀忠etal.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究.遥感学报.2006,10(01)：49-57.
[174]张丽新,王家钦,赵雁南.基于Relief的组合式特征选择.复旦学报(自然科学版),2004,43(5)：894-898
[175]张丽新.高维数据的特征选择及基于特征选择的集成学习研究：[博士学位论文].北京：清华大学,2004.
[176]张隆.基于信息论的特征选择和分类算法研究：[硕士论文].重庆：西南农业大学,2005.
[177]张培林,钱林方,曹建军,等.基于蚁群算法的支持向量机参数优化.南京理工大学学报(自然科学版).2009,33(04)：464-468.
[178]张晓斌.基于网格的文本分类PSE研究：[博士论文].上海：上海大学,2007.
[179]张晓龙,江川.基于AUC的SVM多类分类方法的研究.计算机工程与应用.2007,(14)：166-169.
[180]张秀英,冯学智,丁晓东.基于面向对象方法的IKONOS影像城市植被信息提取.浙江大学学报：农业与生命科学版.2007,33(5)：568-573.
[181]张学工.关于统计学习理论与支持向量机.自动化学报.2000,26(01)：32-42.
[182]张颖娜,张超,赵冬玲,等.利用ReliefFO和海明遗传算法的面向对象分类特征选择算法.武汉大学学报(信息科学版).2010,35(12)：1444-1448.
[183]张颖娜.面向对象高分辨率遥感影像分类的特征选择方法研究：[硕士学位论文].北京：中国农业大学,2010.
[184]张永生,巩丹超等.高分辨率遥感卫星应用--成像模型、处理算法及应用技术.科学出版社,2004.
[185]张振平.基于Bayes错误率上界最小的特征选择算法的研究：[硕士学位论文].上海：同济大学,2006.
[186]赵春晖,陈万海,郭春燕.多类支持向量机方法的研究现状与分析.智能系统学报.2007, (02)：11-17.
[187]赵英时.遥感应用分析原理与方法.北京：科学出版社,2004.
[188]郑春红,焦李成,丁爱玲.基于启发式遗传算法的SVM模型自动选择.控制理论与应用.2006,(02)：187-192.
[189]郑春红,焦李成,郑贵文.基于GA的遥感图像目标SVM自动识别.控制与决策.2005,20(11)：1212-1215+1220.
[190]郑雅敏.基于遗传算法的特征选择方法的改进研究：[硕士论文].重庆：重庆大学,2008.
[191]周辉仁,郑丕谔,任仙玲.最小二乘支持向量机的参数优选方法及应用.系统工程学报.2009,24(02)：248-252.
[192]周俊,晏非,孙曼.基于区域分割合并的建筑物半自动提取方法.海洋测绘,2005,25(1)：58-60.
[193]周绮凤.基于支持向量机的若干分类问题研究：[博士学位论文].福建厦门：厦门大学,2007.
[194]朱莉.基于SVM的煤与瓦斯突出预测模型研究：[硕士论文].西安：西安科技大学,2009.
[2]Canny John. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[3]Chapelle, O., Vapnik, V., Bousquet, O. et al. Choosing multiple parameters for support vector machines. Machine Learning.2002,46(1-3):131-159.
[4]Clausi, D. A., Zhao, Y. Rapid extraction of image texture by co-occurrence using a hybrid data structure. Computers and Geosciences.2002,28(6):763-774.
[5]D Koller, M Sahami. Toward Optimal Feature Selection. Proc of Int'l Conf on Machine Learning.1996.284-292.
[6]Dash J., Steinle E., Singh R.P. et al. Automatic Building Extraction from Laser Scanning Data:an Input Tool for Disaster Management. Advances in Space Research,2004,33:317-322.
[7]Davies S,Russl S.Np-completeness of searches for smallest possible feature sets.Proceedings of the AAAI Fall 94 Symposium on Relevance. Menlo Park,CA.:AAAI Press,1994,37-39
[8]de Diego, I. M., Serrano, A., Conde, C. et al. Face verification with a kernel fusion method. Pattern Recognition Letters.2010.
[9]Duda R E, Hart P E. Pattern Classification and Scene Analysis[M]. New York,Wiley,1973
[10]Duda, R. O., Hart, P. E., Stork, D. G.李宏东,姚天翔译.模式分类(第二版).北京：机械工业出版社,2003.
[11]Foody, G. M., Mathur, A. Toward intelligent training of supervised image classifications:Directing training data acquisition for SVM classification. Remote Sensing of Environment.2004,93(1-2): 107-117.
[12]Frank A, van den Heuvel. Estimation of Interior Orientation Parameters from Constraints on Line [3] Measurements in a Single Image. International Archives of Photogrammetry and Remote Sensing,1999,32(5):81-88
[13]G H John, R Kohavi, K Pfleger. Irrelevant Features and the Subset Selection Problem. Proc of the 11th Int'l Conf on Machine Learning.1994.121-129
[14]Geneletti D., Gorte B. G. H., A Method For Object-Oriented Land Cover Classification Combining Landsat TM Data and Aerial Photographs. International Journal of Remote Sensing, 2003,24 (6):1273-1286.
[15]Gitas, I. Z., Polychronaki, A., Katagis, T. et al. Contribution of remote sensing to disaster management activities:A case study of the large fires in the Peloponnese, Greece. International Journal of Remote Sensing.2008,29(6):1847-1853.
[16]Greunaw. Adaptive Least Square Correlation:A Powerful Image Matching Technique.S Afr J of Photogrammetry, RS and Cartography,1985,14(3):175-187
[17]Grigorescu C., Petkov N., Westenberg M. A.. Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing.2003,12(7):729-739
[18]Haala N., Brenner C. Extraction of Buildings and Trees in Urban Environments. ISPRS Journal of Photogrammetry and Remote Sensing,1999,54(2-3):130-137.
[19]hackelford A. K. and Davis C. H. A. Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multispectral Data over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing,2003,41, (10):2354-2363.
[20]Haralick, R. M, Shanmugam, K., Dinstein, I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics.1973, smc 3(6):610-621.
[21]Hellwich O., Wiedemann C. Object Extraction from High-Resolution Multi-Sensor Image Data. The 3rd International Conference on Fusion of Earth Data. Sophia Antipolis, France, SEEGreCA, Nice,2000.105-115.
[22]Hofmann P. Detecting informal settlements from IKONOS image data using methods of object oriented image analysis-an example from Cape Town (South Africa), In:Remote Sensing of Urban Areas, edited by Jurgens, Carsten (Regensburg),2001,41-52.
[23]Hsu, C. W., Lin, C. J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks.2002,13(2):415-425.
[24]Hu, H., Ban, Y. Urban landuse/land-cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis. In:Proceedings of SPIE-The International Society for Optical Engineering.2008.
[25]Hughes, G. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory.1968,14(1):55-63.
[26]K Kira, LA Rendell. The Feature Selection Problem:Traditional Methods and a New Algorithm. Proc of 9th National Conf on AI.1992.129-134
[27]Kass M., Witkin A., Terzopoulos D.. Snakes:Active contour models. International Journal of Computer Vision,1988,1 (4):321-331
[28]Kettig R. L., Landgrebe D. A. Computer Classification of Remotely Sensed Multispectral Image Data Extraction and Classification of Homogeneous Objects, IEEE Transactions on Geoscience Electronics,1976,14(1):19-26.
[29]Kim,T. A study on the epipolarity of linear pushbroon images. Photogrammetric Engineering and Remote Sensing,2000,66(8):961-966
[30]Koga, T., Iinuma, K., Hirano, A. et al. Motion compensated interframe coding for video conferencing. In:Proc. Nat. Telecommun. Conf.1981.1-5.
[31]Kononenko I. Estimation attributes:analysis and extensions of RELIEF. Proceedings of the 1994 European Conference on Machine Learning,1994,171-182
[32]Lewis P M,The characteristic selection problem in recognition system.IRE Transaction on Information Theory,1962.8:171-178
[33]Li, H. T., Gu, H. Y., Han, Y. S. et al. Object-oriented classification of polarimetric SAR imagery based on statistical region merging and support vector machine. In:2008 International Workshop on Earth Observation and Remote Sensing Applications, EORSA.2008.
[34]Manoranjan Dash,Huan Liu. Feature Selection for Classification. Intelligent Data Analysis,1997, 1(3):131-156
[35]Mariz, C., Gianelle, D., Bruzzone, L. et al. Fusion of multi-spectral SPOT-5 images and very high resolution texture information extracted from digital orthophotos for automatic classification of complex Alpine areas. International Journal of Remote Sensing.2009,30(11):2859-2873.
[36]Marko Robnik-Sikonja, Igor Kononenko. Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal,2003,53:23-69
[37]Navia-Vazquez, A. Compact multi-class support vector machine. Neurocomputing.2007,71(1-3): 400-405.
[38]Nelson, J. D. B., Damper, R. I., Gunn, S. R. et al. A signal theory approach to support vector classification:The sinc kernel. Neural Networks.2009,22(1):49-57.
[39]Nichol J. and Lee. C. M. Urban Vegetation Monitoring in Hong Kong Using High Resolution Multispectral Images. International Journal of Remote Sensing,2005,26(10):903-918.
[40]Oladunni, O. O., Trafalis, T. B., Papavassiliou, D. V. Knowledge-based multiclass support vector machines applied to vertical two-phase flow. In:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2006:188-195.
[41]Opper, M., Winther, O. Gaussian processes and SVM:Mean field and leave-one-out. Advances in Large Margin Classifiers.2000:311-326.
[42]Otto GP,Chau T K. A Region-Growing Algorithm for Matching of Terrain Images. Image and Vision Computing,1989,7(2):83-94
[43]Pal, M., Mather, P. M. Support vector machines for classification in remote sensing. International Journal of Remote Sensing.2005,26(5):1007-1011.
[44]Perez-Cruz, F., Bousquet, O. Kernel methods and their potential use in signal processing. IEEE Signal Processing Magazine.2004,21(3):57-65.
[45]Platt, J., Cristianini, N., Shawe-Taylor, J. Large margin DAGs for multiclass classification. Advances in neural information processing systems.2000,12(3):547-553.
[46]R.Jensen(著)陈晓玲等(译).遥感数字影像处理导论.北京：机械工业出版社,2007.
[47]Rottensteiner F., Trinder J., Clode S. et al. Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images. Image and Vision Computing,2003,60-65.
[48]Ruvimbo G., Philippe D., Morgan D.. Object-oriented change detection for the city of Harare, Zimbabwe. Zimbabwe, Expert Systems with Applications (2007), doi:10.1016/j.eswa.2007.09.067
[49]Scholkopf, B., Smola, A. J., Williamson, R. C. et al. New support vector algorithms. Neural Computation.2000,12(5):1207-1245.
[50]Shahidi, R., Moloney, C. Decorrelating the structure and texture components of a variational decomposition model. IEEE Transactions on Image Processing.2009,18(2):299-309.
[51]Shevade, S. K., Keerthi, S. S., Bhattacharyya, C. et al. Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks.2000,11(5):1188-1193.
[52]Singh, M, Singh, S. Spatial Texture Analysis:A Comparative Study. In:Pattern Recognition,2002. Proceedings.16th International Conference on Pattern Recognition.2002.676-679.
[53]Smith, G. M., Fuller, R. M. An integrated approach to land cover classification:An example in the Island of Jersey. International Journal of Remote Sensing.2001,22(16):3123-3142.
[54]Sohel S., Dare P., Jones S., Automatic Classification of Land Cover Features with High Resolution Imagery and LIDAR Data:an Object Oriented Approach.2005, Proceedings of SSC2005 Spatial Intelligence, Innovation and Praxis:The national biennial Conference of the Spatial Sciences Institute.
[55]T M. Cover. The best two independent measurements are not the two best.IEEE Trans. Syst.Man Cybern,1974,4 (2):116-117
[56]T.E.Chen,R.Shibasaki.3D modeling and visualization of building in area by linear, photogrammetry. Proceeding of UM3, Japan,1998
[57]T.Toussaint. Note on optimal selection of independent binary-valued features for pattern. IEEE: Transactions on information theory.1997,618.
[58]Tarabalka, Y., Chanussot, J., Benediktsson, J. A. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition.2010.
[59]Tatsumi, K., Hayashida, K., Higashi, H. et al. Multi-objective multiclass support vector machine for pattern recognition. In:Proceedings of the SICE Annual Conference.2007.1095-1098.
[60]Tax, D., Duin, R. Support vector domain description. Pattern Recognition Letters.1999,20(11-13): 1191-1199.
[61]Tosun, A. B., Kandemir, M., Sokmensuer, C. et al. Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition.2009,42(6): 1104-1112.
[62]Tupin F, Roux M.3D information extraction by structural Matching of SAR and optical features. http://www.isprs.org/istanbul/comml/papers/88.pdf:ISPRS,2004
[63]Tupin F, Roux M. Detection of building outlines based on the fusion of SAR and optical features. ISPRS Journel of Photogrammetry & Remote Sensing,2003,58:71.
[64]Tupin F. Merging of SAR and optical features for 3D reconstruction in a radargrammetric framework. In:International Geoscience and Remote Sensing Symposium, Alaska,2004,126-129
[65]Vapnik, V. N. An overview of statistical learning theory. In:IEEE Transactions on Neural Networks; 1999:988-999.
[66]Vapnik, V. N. Statistical Learning Theory. New York:John Wiley & Sons,1998.
[67]Vapnik, V. N.,张学工译.统计学习理论的本质.北京：清华大学出版社,2000.
[68]Vapnik, V., Chapelle, O. Bounds on error expectation for support vector machines. Neural Computation.2000,12(9):2013-2036.
[69]Vapnik. V., Chervonenkis, A. On uniform convergence of the frequencies of events to their probabilities. Teoriya Veroyatnostei i ee Primeneniya.1971,16(2):264-279.
[70]Walter, V. Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing.2004,58(3-4):225-238.
[71]Wang L. Integration of Object-Based and Pixel-Based Classification for Mapping Mangroves with IKONOS Imagery. International Journal of Remote Sensing,2004,25(24):5655-5668.
[72]Wang, L., Sousa, W. P., Gong, P. et al. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment.2004, 91(3-4):432-440.
[73]Weidner U and Forstner W. Towards automatic building extraction from high resolution digital elevation models. ISPRS Journal,1995,50(4)
[74]Wesaka, J. S., Rosenfeld, A., Dyer, C. R. A Comparative Study of Texture Measures for Terrain Classification. IEEE Trans. Syst., Man. & Cybern.1976,6:269-285.
[75]Wharton S. A. Spectral-Knowledge-Based Approach for Urban and Land-cover Discrimination, IEEE Transactions on Geoscience and Remote Sensing,1987,25(3):272-282.
[76]Wu, K. P., Wang, S. D. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognition.2009,42(5):710-717
[77]Xie, Z., Roberts, C., Johnson, B. Object-based target search using remotely sensed data:A case study in detecting invasive exotic Australian Pine in south Florida. ISPRS Journal of Photogrammetry and Remote Sensing.2008,63(6):647-660.
[78]Zaakkola, T. S., Haussler, D. Probabilistic kernel regression models. In:Proceedings of the 1999 Conference on AI and Statistics.1999.
[79]Zhan Q. M., Molenaar M., Tempfli K. Hierarchical Image-Object based Structural Analysis toward Urban Land Use Classification using High Resolution Imagery and Airborne LIDAR Data Proceedings of the 3rd International Symposium on Remote Sensing of Urban Areas,2002.
[80]薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析.电子学报.2006,34(1)：155-158,134.
[81]蔡涛,王润生.一个从多波段遥感图像提取道路网的算法.软件学报,2001,12(6)：943-948
[82]曹淑娟.核空间中的平分最近点法与模糊支持向量机：[硕士学位论文].武汉：华中科技大学,2006.
[83]曹雪,柯长青.基于对象级的高分辨率遥感影像分类研究.遥感信息.2006,(5)：27-30,51.
[84]陈金丽.面向对象的最邻近算法研究与实现：[硕士学位论文].北京：中国地质大学(北京),2009.
[85]陈亮,张友静,陈波.结合多尺度纹理的高分辨率遥感影像决策树分类.地理与地理信息科学.2007,23(4)：18-21.
[86]陈敏捷.基于支持向量机的面向对象高分辨率遥感影像分类方法研究：[硕士学位论文].北京：中国农业大学,2010.
[87]陈启浩,高伟,刘修国.辅以纹理特征的高分辨率遥感影像分类.测绘科学.2008,33(1)：88-90.
[88]陈庆华,王雷QuickBird影像在城市规划监察中的应用.交通科技与经济,2007,9(6)：61-62
[89]陈书海,傅录祥.实用数字图像处理.科学出版社,2005
[90]陈述彭,谢传节.城市遥感与城市信息系统.测绘科学.2000,25(1)：1-9.
[91]陈云浩,冯通,史培军.基于面向对象和规则的遥感影像分类研究.武汉大学学报：信息科学版.2006,31(4)：316-320.
[92]陈忠.高分辨率遥感图像分类技术研究：[博士学位论文].北京：中国科学院研究生院(遥感应用研究所),2006.
[93]崔丽娜,龚威平,郝惠.利用卫星遥感技术辅助城乡规划督察成果应用研究—以2008～2011年郑州市动态监测成果为例.Urban Studies,2012,19(6):10-14.
[94]党安荣.遥感技术在城市规划研究中的应用.小城镇建设,2001,7：22-23
[95]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004.
[96]邓乃扬,田英杰.支持向量机--理论、算法与拓展.北京：科学出版社,2009.
[97]杜凤兰,田庆久,夏学齐.面向对象的地物分类法分析与评价.遥感技术与应用.2004,19(1)：20-23.
[98]冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究.北京测绘.2007,(3)：19-22.
[99]付静.面向旋转机械的支持向量机故障智能诊断方法及实验研究：[硕士论文].赣州市：江西理工大学,2008.
[100]高瑞莲,邹凤,周海波.多源遥感数据融合在城市发展研究中的应用.信阳师范学院学报(自然科学版),2001,01：114-117
[101]宫鹏,黎夏,徐冰.高分辨率影像解译理论与应用方法中的一些研究问题.遥感学报,2006,10(1)：1-5
[102]巩丹超,张永生.基于航空影像的建筑物半自动提取技术研究.测绘通报,2002,10：15-18
[103]郭春燕.基于支持向量机的高光谱遥感图像分类：[硕士论文].哈尔滨：哈尔滨工程大学,2007.
[104]郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究.系统仿真学报.2006,18(07)：2033-2036+2051.
[105]郭雷,肖怀铁,付强.基于支持矢量机特征空间距离的多目标分类方法.计算机仿真.2005,(09)：272-274.
[106]贺江兵.城区建设中合理的城市规划策略,科技创新导报报,2011,21：112.
[107]侯蕾,尹东,尤晓建.一种遥感图像中建筑物的自动提取方法.计算机仿真,2006,4(23)：184-187
[108]胡慧萍.面向对象分类技术的景观信息获取：[硕士学位论文].长沙：中南大学,2007.
[109]胡亚明,王明厉.应用遥感技术实现城市规划动态监察.城市勘测,2005,2：22-24
[110]黄慧萍,吴炳方,李苗苗.高分辨率影像城市绿地快速提取技术与应用.遥感学报,2004,8(1)：68-69.
[111]黄慧萍,吴炳方.地物大小、对象尺度、影像分辨率的关系分析.遥感技术与应用.2006,21(3)：243-248.
[112]黄景涛,马龙华,钱积新.基于统计试验设计方法的支持向量机参数选取.电路与系统学报.2008,(06).
[113]黄景涛.支持向量机算法参数选择及其在电站锅炉系统中的应用研究：[博士学位论文].浙江杭州：浙江大学,2005.
[114]惠文华.基于支持向量机的遥感图像分类方法.地球科学与环境学报.2006,28(2)：93-95.
[115]姜波,陈庆华,于宁.遥感技术在哈尔滨市数字城市规划中的应用.交通科技与经济,2007,2：76-77；79
[116]孔春芳,吴冲龙,徐凯.基于多特征的城市用地高分辨率影像分类与提取.数据采集与处理报,2007,22(3)：315-320.
[117]李成范.城市土地利用变化的遥感监测：[硕士学位论文].重庆：西南大学,2009.
[118]李德仁.论21世纪遥感与GIS的发展.武汉大学学报(信息科学版).2003,(02)：127-131.
[119]李蓉,叶世伟,史忠植.SVM-KNN分类器——一种提高SVM分类精度的新方法.电子学报.2002,(05)：745-748.
[120]李坤,徐维朴,郑南宁,等.一种新的基于遗传算法的快速运动估计方法.电子学报.2000,(06)：114-117.
[121]梁松.城市规划动态监管卫星遥感关键技术研究：[博士学位论文].北京：中国矿业大学,2009.
[122]廖圣东,廖其芳,曾文华,等.高分辨率遥感影像在城市二维景观建立中的应用.国土资源遥感,2006,4(70)：40-42.
[123]林升梁,刘志.基于RBF核函数的支持向量机参数选择.浙江工业大学学报.2007,35(02)：163-167
[124]林贤金.基于信息理论的入侵检测技术研究：[硕士论文].福州：福州大学,2002.
[125]刘建贵,张兵,郑兰芬,等.基于光谱特征的城市人工地物分级分类方法研究.遥感技术与应用,1999,14(3)：1-6.
[126]刘龙飞,陈云浩,李京.遥感影像纹理分析方法综述与展望.遥感技术与应用.2003,18(6)：441-447.
[127]刘志刚,史文中,李德仁.一种基于支撑向量机的遥感影像不完全监督分类新方法.遥感学报.2005,9(4)：363-373.
[128]刘志刚.支撑向量机在光谱遥感影像分类中的若干问题研究：[博士学位论文].湖北武汉：武汉大学,2004.
[129]陆景辉.基于信息理论的特征选择算法研究：[硕士论文].北京：北京交通大学,2007.
[130]罗名海.3S技术的发展趋势与在城市规划中的应用前景.地理空间信息,2004,02(4)：4-7
[131]马晶,余洁,张继贤,等.影像融合技术在土地利用动态遥感监测中的应用研究.测绘科学,2007,32(2)：128-130.
[132]马廷.高分辨率卫星影像及其信息处理的技术模型.遥感信息.2001,(03)：6-10.
[133]马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用.控制与决策.2003,(03)：272-276+284.
[134]蒲智,刘萍,杨辽,葛挺进.面向对象技术在城市绿地信息提取中的应用.福建林业科技,2006,33(1)40-44.
[135]齐志泉,田英杰,徐志洁.支持向量机中的核参数选择问题.控制工程.2005,12(04)：379-381.
[136]钦伟瑾.基于对象的城市遥感影像分类方法及应用研究：[硕士学位论文].西安：长安大学,2009.
[137]曲瀛.硬件演化技术及其应用基础研究：[硕士论文].南京：南京航空航天大学,2002.
[138]荣海娜,张葛祥,金炜东.系统辨识中支持向量机核函数及其参数的研究.系统仿真学报.2006, 18(11)：3204-3208+3226.
[139]沈琪,马金辉.高分辨率遥感数据在现代城市规划中的应用.甘肃科学学报,2006,18(1)：44-48
[140]宋刚贤.基于遥感影像的城市违规建设用地监测研究：[硕士学位论文].南京：南京农业大学,2008.
[141]宋晓宇,单新建.高分辨率卫星影像在城市建筑物识别中的初步应用.遥感信息,2002,1：27-31
[142]苏伟,李京,陈云浩等,基于多尺度影像分割的面向对象城市土地覆被分类研究--以马来西亚基隆坡市城市中心区为例,遥感学报,2007,11(4)：530-539.
[143]孙旭红.遥感系统、地理信息系统在城市规划监测中的应用.科学技术与工程,2006,6(11)：1576-1579；1588.
[144]孙燕,李利军,王乘等.3S支持下违章建筑监管平台架构及关键技术。计算机技术与发展,2006,16(10)：174-176.
[145]唐亮,谢维信,黄建军等.从航空影像中自动提取高层建筑物.计算机学报,2005,7(28)：1199-1204.
[146]汪波,龚威平,孙越,刘树军.卫星遥感数据在城乡规划动态监测中的应用.航天器工程,2011,20(6)：113-117.
[147]汪求来.面向对象遥感影像分类方法及其应用研究：[硕士学位论文].江苏南京：南京林业大学,2008.
[148]王辉.基于核主成分分析特征提取及支持向量机的人脸识别应用研究：[硕士学位论文].安徽合肥：合肥工业大学,2006.
[149]王琳,徐涵秋,李胜.福州城市扩展的遥感动态监测.地球信息科学,2006,4(8)：129-135
[150]王荣静.基于svm的MODIS数据土地覆盖分类方法研究：[硕士学位论文].北京：中国农业大学,2005.
[151]王亚旗.“城中村”改造模式探究：[硕士论文].西安：西安建筑科技大学,2010.
[152]王祖伟,秦其明.多源遥感数据融合及在城市研究中的应用.测绘通报,2002,03：22-24
[153]王佐成.基于纹理的遥感图像分类研究：[博士学位论文].四川成都：西南交通大学,2007.
[154]韦娜,王涛.结合ReliefF与支持向量机的特征选择方法研究.计算机应用与软件.2008,1：283-285.
[155]魏飞鸣.基于对象信息的遥感影像分类研究：[硕士学位论文].成都：电子科技大学,2008.
[156]魏玉璋.基于QuickBird影像的土地利用信息提取技术研究：[硕士学位论文].浙江杭州：浙江大学,2006.
[157]吴海平.用高分辨率IKONOS影像测定城市建筑容积率的研究[硕士学位论文].北京：中国农业大学,2003
[158]吴健平,张立.卫星遥感技术在城市规划中的应用.遥感技术与应用,2003,18(1)：52-56.
[159]吴琳琳.基于SVM技术的手写数字识别的研究：[硕士论文].济南：山东师范大学,2006.
[160]徐芳.航空影像中地物识别的SVM方法研究.测绘通报.2004,(02)：34-36.
[161]徐振华,韦松林,张燕妮等.3S技术的发展趋势及其在城市规划中的应用前景.科技情报开发与经济,2005,15(12)：139-141.
[162]薛晨荣.形状特征在交通类目标识别中的应用：[硕士学位论文].安徽合肥：中国科学技术大学, 2008.
[163]颜根廷,李传江,马广富.基于混合遗传算法的支持向量机参数选择.哈尔滨工业大学学报.2008,40(05)：688-691.
[164]杨惠岚.多源卫星遥感影像在城市规划中的应用.科技情报开发与经济,2006,16(19)：145-146；149.
[165]杨利英,覃征,王卫红.多分类器融合系统设计与应用,计算机工程,2005,31(5)：175-177.
[166]杨宁.高分辨率影像面向对象分类特征选择方法研究：[硕士论文].西安：西安科技大学,2012.
[167]杨希明.高光谱遥感图像分类方法研究：[硕士论文].哈尔滨：哈尔滨工程大学,2007.
[168]殷小舟.一种改进的结合K近邻法的SVM分类算法.中国图象图形学报.2009,(11)：2299-2303.
[169]于静,程鸿.加强城市遥感影像技术在城市规划监控中的应用.城乡建设,2007,03：63-64
[170]袁胜发,褚福磊.SVM多类分类算法及其在故障诊断中的应用.振动工程学报.2004,17(S)：419-421.
[171]张桂峰.粒度理论下的多尺度遥感影像分割：[博士学位论文].武汉：武汉大学,2010.
[172]张建国,李宪文,吴延磊.面向对象的冬小麦种植面积遥感估算研究.农业工程学报.2008,24(5)：156-160.
[173]张锦水,何春阳,潘耀忠etal.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究.遥感学报.2006,10(01)：49-57.
[174]张丽新,王家钦,赵雁南.基于Relief的组合式特征选择.复旦学报(自然科学版),2004,43(5)：894-898
[175]张丽新.高维数据的特征选择及基于特征选择的集成学习研究：[博士学位论文].北京：清华大学,2004.
[176]张隆.基于信息论的特征选择和分类算法研究：[硕士论文].重庆：西南农业大学,2005.
[177]张培林,钱林方,曹建军,等.基于蚁群算法的支持向量机参数优化.南京理工大学学报(自然科学版).2009,33(04)：464-468.
[178]张晓斌.基于网格的文本分类PSE研究：[博士论文].上海：上海大学,2007.
[179]张晓龙,江川.基于AUC的SVM多类分类方法的研究.计算机工程与应用.2007,(14)：166-169.
[180]张秀英,冯学智,丁晓东.基于面向对象方法的IKONOS影像城市植被信息提取.浙江大学学报：农业与生命科学版.2007,33(5)：568-573.
[181]张学工.关于统计学习理论与支持向量机.自动化学报.2000,26(01)：32-42.
[182]张颖娜,张超,赵冬玲,等.利用ReliefFO和海明遗传算法的面向对象分类特征选择算法.武汉大学学报(信息科学版).2010,35(12)：1444-1448.
[183]张颖娜.面向对象高分辨率遥感影像分类的特征选择方法研究：[硕士学位论文].北京：中国农业大学,2010.
[184]张永生,巩丹超等.高分辨率遥感卫星应用--成像模型、处理算法及应用技术.科学出版社,2004.
[185]张振平.基于Bayes错误率上界最小的特征选择算法的研究：[硕士学位论文].上海：同济大学,2006.
[186]赵春晖,陈万海,郭春燕.多类支持向量机方法的研究现状与分析.智能系统学报.2007, (02)：11-17.
[187]赵英时.遥感应用分析原理与方法.北京：科学出版社,2004.
[188]郑春红,焦李成,丁爱玲.基于启发式遗传算法的SVM模型自动选择.控制理论与应用.2006,(02)：187-192.
[189]郑春红,焦李成,郑贵文.基于GA的遥感图像目标SVM自动识别.控制与决策.2005,20(11)：1212-1215+1220.
[190]郑雅敏.基于遗传算法的特征选择方法的改进研究：[硕士论文].重庆：重庆大学,2008.
[191]周辉仁,郑丕谔,任仙玲.最小二乘支持向量机的参数优选方法及应用.系统工程学报.2009,24(02)：248-252.
[192]周俊,晏非,孙曼.基于区域分割合并的建筑物半自动提取方法.海洋测绘,2005,25(1)：58-60.
[193]周绮凤.基于支持向量机的若干分类问题研究：[博士学位论文].福建厦门：厦门大学,2007.
[194]朱莉.基于SVM的煤与瓦斯突出预测模型研究：[硕士论文].西安：西安科技大学,2009.