遥感影像数据挖掘技术研究
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摘要
随着传感器技术的发展,遥感影像的数量以飞快地速度增长。人们收集和存储影像的能力已经大大超过分析和从影像上获取信息的能力。这一切促使我们发展图像挖掘技术,它需各学科如图像处理、数据库、信息提取、机器学习和软件设计等同仁共同付出努力。图像挖掘旨在发现隐藏在数据库中含蓄的不明确的知识、影像数据的关系或其它模式,是数据挖掘的一个重要分支。
遥感图像数据挖掘(remote sensing image mining,(ReSIM))技术不仅是图像挖掘技术在遥感领域的应用,也是空间数据挖掘技术的一个重要拓展分支。它既要应用图像挖掘的一般性的理论和技术,又要结合遥感数据和空间数据的特殊性如独特的空间位置信息、复杂的空间关系和空间尺度,是空间数据挖掘与图像挖掘交叉的研究学科。其中,分类和预测方法是遥感图像分析和信息挖掘的重要研究内容,也是研究的重点。
本文围绕遥感影像信息自动化与智能化的获取和利用这一线索,对遥感影像数据挖掘理论和技术进行了研究,主要的研究成果和创新点如下:
(1)研究了功能驱动和信息驱动两种图像挖掘模式,提出了信息驱动的遥感影像挖掘原型系统结构图和流程图,并指出系统应具备的功能和必要的工具。
(2)实现了最常用的两种分类器一监督分类(bayes)和非监督分类(Isodata)算法,并提出了bayes算法改良方法;实现了灰度共生矩阵纹理表示法;研究了图像中对象的空间结构和空间关系。
(3)在研究几种数据挖掘理论如模糊分类法、证据理论、人工神经网络(BP算法和SOM网络)、支持向量机、关联规则、决策树算法基础上,提出了基于这几种理论的遥感影像挖掘方法。
(4)上述的数据挖掘方法都是针对像元的图像分析,所能够得到的信息是极其有限的,不能够反映像邻域间的上下文信息(contextual information)。提出了“面向对象”图像挖掘方法,给出了该方法的流程和算法,实现了该流程和算法。
(5)在研究知识——颜色、纹理和边界等语义特征、混合光谱特征、高维数据的特征、GIS数据、地学专家知识等表示方法的基础上,提出了GIS数据辅助遥感图像数据挖掘的两种途径——以逻辑波段形式直接参与分类和融于空间数据库中系统化应用,并给出应用模型或系统框架结构;指出挖掘查询语言应为类似SQL的适用于地理信息挖掘的输入请求语言——GMQL(Geo-Mining Query Language);提出了知识库的表示方法;实现了基于规则化知识库遥感图像挖掘方法;实现了基于数据降维的高维数据特征提取算法。
(6)在分析Web环境下数据挖掘现状的基础上,提出了Web环境下图像挖掘系统框架图;进一步提出了Web环境下遥感影像数据挖掘系统框架图。
The volume of remote sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. Image mining is armed to find the implicit and ambiguous informations, relationship of image data or other patterns in database.Remote sensing image data mining isn't only the applications of image mining in the field of remote sensing, but also a important embranchment of the spacial data mining.It uses the ecumenical theory and technology of image mining as well as combines the particularity of remote sensing image and spacial data, such as the particular spacial locations, the complicated spatial relations and the spatial scale. It is a important crossed research subject between the spacial data mining and image mining. Finally, the method of classification and forecast is an important research field in the remote sensing image analysing and mining, also, is a reaearch focus of this dissertation.Encircling a key of remote sensing image information automatically acquired and used, mainly studies the theory and technology in remote sensing image data mining. The main reaearch results and creative proposals are following :(1) Studying two frameworks for image mining---an information-driven frameworkand a funtion-driven framework, presents a framework figure of a prototype system for remote sensing image mining and a flow chart of it. Also, points out necessaryfunctions and tools for the system.(2) Achieves the algorithm of two frequently used classification implement—a supervised and non.supervised, bayes and isodata algorithm. Also improves on the bayes algorithm. Achieves texture feature extraction by using gray-level co-occurrence matrices. Studys the special structure and relations of image objects.(3) On the basis of studying of a few of data mining theories, including fuzzy classification, evidence theory, artificial nerve network (BP and SOM), support vector machines, association rules and decision-tree algorithm, presents the method of remote sening mining based on the theorys.(4) The above-mentioned methods of data mining almost aims at the image analyzing of pixels. The information acquiring from pixels is greadtly limited, which don't show the contextual information of neighboring pixels. In the dissertation, presents the " Object Oriented "image mining concept for the first time. Also, designs the flow and algorithm of the method and realizes it.(5) In research of the information of expression, including semantic feature, such as color, texture, border, as so on, spectral mixture feature, high- dimension data feature extraction,Gis data and expert information,presents two approach by using Gis data for image mining—straightway classifying in form of logistic wave
band and systematizing applicat ion in the spatial database. Designs the using model or system framework. Points out mining Language would be the same with geographical information mining, resembling SQL input request Language—GMQL (Geo-Mining Query Language). Presents a expression method of a knowledge.data base. Realizes the remote sensing image data mining based on rule. Realizes high, dimension data feature extraction algorithm by using debasing dimension of data.(6) After analyzing the actuality of web data mining, brings forward a framework of the web image mining system. On second thoughts, puts forward a framework of the web Remote sensing image mining system.
遥感图像数据挖掘(remote sensing image mining,(ReSIM))技术不仅是图像挖掘技术在遥感领域的应用,也是空间数据挖掘技术的一个重要拓展分支。它既要应用图像挖掘的一般性的理论和技术,又要结合遥感数据和空间数据的特殊性如独特的空间位置信息、复杂的空间关系和空间尺度,是空间数据挖掘与图像挖掘交叉的研究学科。其中,分类和预测方法是遥感图像分析和信息挖掘的重要研究内容,也是研究的重点。
本文围绕遥感影像信息自动化与智能化的获取和利用这一线索,对遥感影像数据挖掘理论和技术进行了研究,主要的研究成果和创新点如下:
(1)研究了功能驱动和信息驱动两种图像挖掘模式,提出了信息驱动的遥感影像挖掘原型系统结构图和流程图,并指出系统应具备的功能和必要的工具。
(2)实现了最常用的两种分类器一监督分类(bayes)和非监督分类(Isodata)算法,并提出了bayes算法改良方法;实现了灰度共生矩阵纹理表示法;研究了图像中对象的空间结构和空间关系。
(3)在研究几种数据挖掘理论如模糊分类法、证据理论、人工神经网络(BP算法和SOM网络)、支持向量机、关联规则、决策树算法基础上,提出了基于这几种理论的遥感影像挖掘方法。
(4)上述的数据挖掘方法都是针对像元的图像分析,所能够得到的信息是极其有限的,不能够反映像邻域间的上下文信息(contextual information)。提出了“面向对象”图像挖掘方法,给出了该方法的流程和算法,实现了该流程和算法。
(5)在研究知识——颜色、纹理和边界等语义特征、混合光谱特征、高维数据的特征、GIS数据、地学专家知识等表示方法的基础上,提出了GIS数据辅助遥感图像数据挖掘的两种途径——以逻辑波段形式直接参与分类和融于空间数据库中系统化应用,并给出应用模型或系统框架结构;指出挖掘查询语言应为类似SQL的适用于地理信息挖掘的输入请求语言——GMQL(Geo-Mining Query Language);提出了知识库的表示方法;实现了基于规则化知识库遥感图像挖掘方法;实现了基于数据降维的高维数据特征提取算法。
(6)在分析Web环境下数据挖掘现状的基础上,提出了Web环境下图像挖掘系统框架图;进一步提出了Web环境下遥感影像数据挖掘系统框架图。
The volume of remote sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. Image mining is armed to find the implicit and ambiguous informations, relationship of image data or other patterns in database.Remote sensing image data mining isn't only the applications of image mining in the field of remote sensing, but also a important embranchment of the spacial data mining.It uses the ecumenical theory and technology of image mining as well as combines the particularity of remote sensing image and spacial data, such as the particular spacial locations, the complicated spatial relations and the spatial scale. It is a important crossed research subject between the spacial data mining and image mining. Finally, the method of classification and forecast is an important research field in the remote sensing image analysing and mining, also, is a reaearch focus of this dissertation.Encircling a key of remote sensing image information automatically acquired and used, mainly studies the theory and technology in remote sensing image data mining. The main reaearch results and creative proposals are following :(1) Studying two frameworks for image mining---an information-driven frameworkand a funtion-driven framework, presents a framework figure of a prototype system for remote sensing image mining and a flow chart of it. Also, points out necessaryfunctions and tools for the system.(2) Achieves the algorithm of two frequently used classification implement—a supervised and non.supervised, bayes and isodata algorithm. Also improves on the bayes algorithm. Achieves texture feature extraction by using gray-level co-occurrence matrices. Studys the special structure and relations of image objects.(3) On the basis of studying of a few of data mining theories, including fuzzy classification, evidence theory, artificial nerve network (BP and SOM), support vector machines, association rules and decision-tree algorithm, presents the method of remote sening mining based on the theorys.(4) The above-mentioned methods of data mining almost aims at the image analyzing of pixels. The information acquiring from pixels is greadtly limited, which don't show the contextual information of neighboring pixels. In the dissertation, presents the " Object Oriented "image mining concept for the first time. Also, designs the flow and algorithm of the method and realizes it.(5) In research of the information of expression, including semantic feature, such as color, texture, border, as so on, spectral mixture feature, high- dimension data feature extraction,Gis data and expert information,presents two approach by using Gis data for image mining—straightway classifying in form of logistic wave
band and systematizing applicat ion in the spatial database. Designs the using model or system framework. Points out mining Language would be the same with geographical information mining, resembling SQL input request Language—GMQL (Geo-Mining Query Language). Presents a expression method of a knowledge.data base. Realizes the remote sensing image data mining based on rule. Realizes high, dimension data feature extraction algorithm by using debasing dimension of data.(6) After analyzing the actuality of web data mining, brings forward a framework of the web image mining system. On second thoughts, puts forward a framework of the web Remote sensing image mining system.
引文
[1] 邸凯昌.空间数据发掘与知识发现[M].武汉大学出版社,2002.12.
[2] 范明、孟小峰等译.数据挖掘[M].机械工业出版社,2001
[3] Mengjie Zhang. Neural Networks for Multiclass Object Mining. Computer Science and Technology. May 19-21, 2003 Cancum, Mexico, 214-218
[4] 李德仁、王树良、李德毅等.论空间数据挖掘和知识发现的理论与方法[J].武汉大学学报.信息科学版.Vol(7).2003年.221-233.
[5] 李德仁.王树良.史文洲等.论空间数据挖掘和知识发现[M].科学出版社,2000.
[6] 黄添强.关于空间数据挖掘环境调控空间决策支持系统研究[D].福州大学硕士学位论文,2003.
[7] 李丙春.基于医学影像的计算机模式分类技术研究与应用[D].西北大学硕士学位论文,2004.
[8] 斐韬、周成虎、骆剑成.空间数据知识发现研究进展评述.
[9] 游代安.GIS辅助的遥感图像处理与分析技术的研究[D].中国人民解放军测绘学院学位论文,2001
[10] 李德仁、关泽群.空间信息系统集成与实现[M].武汉大学出版社,2002
[11] 承继成等.数字地球导论[M].科学出版社,2000.
[12] 杨崇俊.“数字地球”周年综述[J].测绘软科学研究.1999.5(3)
[13] 陈述彭,鲁学军,周成虎.地理信息系统导论[M].科学出版社,2000
[14] 朱述龙、张占睦.遥感图像获取与分析[M].科学出版社,2000
[15] 周成虎等.遥感影像地学理解与分析[M].科学出版社,1999.
[16] Jiawei Han and Micheline Kamber. Data Mining, Concepts and Techniques. Morgan Kaufmann, 2001.
[17] M. Antonie, O. Zaiane, and A. Coman. Application of data mining techniques for medical image classification. In Proceedings of Second International Workshop for Multimedia Data Mining (MDM/KDD'2001) in conjunction with ACM SIGKDD conference, pages 94-101, 2001.
[18] Y. Rui, T. S. Huang, and S. Chang. Image retrieval: Current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10(4): 39-62, 1999. Http//www.esa.asu.edu
[19] Active learning for classifying a spectrally variable subject-(Dr. Patricia G. Foschi and Dr. Huan Liu), 2002. In Proceedings of the 2nd Pattern Recognition for Remote Sensing Workshop (PRRS2002), 16 August 2002, Niagara Falls, Canada, pp. 115-124.
[20] Class-Specific Ensembles for Active Learning Digital Imagery-Amit Mandvikar, and Huan Liu. Accepted as a student paper for the SIAM International Conference on Data Mining. Florida, 2004.
[21] 马超飞.基于关联规则的遥感数据挖掘与应用[D].中国科学院博士研究生学位论文,2002.
[22] 张建沛.支持向量机在数据挖掘中的应用[D].哈尔滨工程大学硕士研究生学位论文,2002.
[23] 王旭红、周明全、耿国华.遥感影像信息挖掘的框架研究[J].计算机应用与软件.(已收录)
[24] Ji Zhang, Wyrme Hsu, Mong Li Lee. Image Mining: Trends and Developments. 2001, http://www.comp.nus.edu.sg/～whsu/publication.
[25] Zhang Ji, Hsu, Mong, Lee, Image Mining: Issues, Frameworks And Techniques, Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), in conjunction with ACM SIGKDD conference. San Francisco, USA, August 26, 2001.
[26] Petra Perner. Image mining: issues, framework, a generic tool and its application to medical-image diagnosis[J]. Engineering Applications of Artificial Intelligence 15(2002) 205-216 http://www.elsevise.com/locate/engappai
[27] Ji Zhang, Wynne Hsu, Mong Li Lee, An Information-driven Framework for Image Mining. 2001, http://www.elsevise.com/locate/engappai
[28] Perner, P., 1998. Case-based reasoning for the low-level and high-level unit of an image interpretation system. In: Singh, S. (Ed.), Advances in Pattern Recognition. Springer, Berlin, pp. 45-54.
[29] 邸凯昌,李德仁,李德毅.空间数据挖掘和知识发现的框架[J].武汉测绘科技大学学报,1997,2(4):328～332.
[30] 邸凯昌.空间数据挖掘和知识发现的理论与方法[D].武汉:武汉测绘科技大学,1999.
[31] J. S. D. Bonet. Image Preprocessing for Rapid Selection in "Pay attention mode". MIL, 2000.
[32] M. C. Burl et al. Mining for Image Content. In Systemics, Cybernetics, and Information Systems: Analysis and Synthesis, (Orlando, FL), July 1999.
[33] S. Gibson et al. Intelligent Mining in image Databases, with Applications to Satellite Imaging and Web Search, Data Mining and Computational Intelligence, Springer-Verlag Berlin, 2001.
[34] R. Kazman and J. Kominek. Information Organization in Multimedia Resources. In proceedings of the 11th annual international conference on Systems documentation, Pages 149-162, 1993.
[35] Y. Rui, T. S. Huang et al. Image Retrieval: Past, Present and Future. Invited paper in Int Symposium on Multimedia Information Processing, Taipei, Taiwan, Dec 11-13, 1997.
[36] M. Flickner, H. S. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, P. Yanker. Query by Image and Video Content: The QBIC system, in IEEE Computer Volume 28 No. 9, pp 23-32, 1995.
[37] J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. Jain, C-F. Shu. Virage Image Search Engine: An Open Framework for Image Management, in Storage and Retrieval for Image and Video Databases (SPIE), pp 76-87, 1996.
[38] J. Feder. Towards Image Content-based Retrieval for the World-Wide Web", Advanced Imaging 11(1), 26-29, 1996.
[39] A. Pentland, R. Picard, and S. Sclaroff. Photobook: Tools for Content-Based Manipulation of Image Databases. SPIE Storage and Retrieval of Image & Video Databases Ⅱ, Feb 1994
[40] V. E. Ogle, and M. Stonebraker. Chabot: Retrieval from a Relational Database of Images, in IEEE Computer Volume 28 No. 9, pp 40-48, 1995.
[41] J. R. Smith and S. F. Chang. VisualSEEk: A Fully Automated Content-based Image Query System, in Proceedings of the 4th ACM International Multimedia Conference and Exhibition, Boston, MA, USA, 11. 1996.
[42] S. Mehrotra, K. Chakrabarti, M. Ortega, Y, Rui, and T. S. Huang. Towards Extending Information Retrieval Techniques fro Multimedia Retrieval. In 3rd International Workshop on Multimedia Information Systems, Como, Italy, 1997.
[43] C. Nastar, M. Mitschke, C. Meilhac, and N. Boujemaa. Surfimage: a Flexible Content-based Image Retrieval System. In ACM-Multimedia, Bristol, England, September 12-16 1998.
[44] R. Manmatha and S. Ravela. A Syntactic Characterization of Appearance and its Application to Image Retrieval. In Proceedings of the SPIE conference on Human Vision and Electronic Imaging Ⅱ, Vol, 3016, San Jose, CA, Feb. '97, 1997.
[45] N. Katayama and S. Satoh. The SR-tree: An Index Structure for High-dimensional Nearest Neighbor Queries. In proceedings of the 1997 ACM SIGMOD Conference, pages 369-380, Tucson, Arizona, May 1997.
[46] K. Lin, H. V. Jagadish and C. Faloutsos. The TV-tree: An Index Structure for High-Dimensional Data. The VLDB Journal, 3 (4): 517-542, 1994.
[47] S. Berchtold, D. A. Keim and H. P. Kriegel. The X-tree: An Index Structure for High-dimensional Data. In Proceedings of the 22nd VLDB Conference, pages 28-39, Mumbai, India, September 1996.
[48] B. C. Ooi, K. L. Tan, C. Yu and S. Bressan. Indexing the Edges-A Simple and Yet Efficient Approach to High-Dimensional Indexing, 19th ACMSIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 166-174, Dallas, Texas, May 2000.
[49] Y. Rui, T. S. Huang et al. Image Retrieval: Past, Present and Future. Invited paper in Int Symposium on Multimedia Information Processing, Taipei, Taiwan, Dec 11-13, 1997.
[50] A. Guttman. R-tree: A Dynamic Index Structure for Spatial Searching. ACM SIGMOD, 1984.
[51] S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
[52] L. Bruzzone and D. E Prieto. Unsupervised Retraining of a Maximum Likelihood Classifier for the Analysis of Multitemporal Remote Sensing Images, IEEE Transactions on Geoscience andRemote Sensing, Volume: 39 Issue: 2, pp 456-460, Feb. 2001.
[53] O. R. Zaiane, J. W. Han et al. Mining MultiMedia Data. CASCON'98: Meeting of Minds, pp 83-96, Toronto, Canada, November 1998.
[54] J. Z. Wang, J. Li et al. System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms. Interactive Distributed Multimedia Systems and Telecommunication Services, Proceedings of the Fourth European Workshop (IDMS'97), 1997.
[55] T. Sellis, N. Roussopoulos and C. Faloutsos. The R+ Tree: A Dynamic Index for Multi-dimensional Objects. In Proc 12th VLDB, 1987
[56] A. Kitamoto. Data Mining for Typhoon Image Collection. In Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), San Francisco, CA, USA, August, 2001
[57] A. K. Jain, M. N. Murty and P. J. Flynn. Data Clustering: A Review. ACM computing survey, Vol. 31, No. 3, September 1999.
[58] 郭达志,胡召玲,陈云浩.GIS中空间对像的不确定性研究.中国矿业大学学报(自然科学版),2000,29(1):20～24.
[59] Lee Es. Neuro_fuzzy Estimation in Spatial Statistics. Journal of Mathematical Analysis and Applications, 2000, 249: 221～231.
[60] 周海燕,王家耀,吴升.空间数据挖掘技术及其应用[J].测绘通报,2002,11(2)11～13.
[61] Pawlak Z. 1982. Rough Sets. Int. Joumal of Computer and Information Sciences, 11 (5)
[62] Ziarko W (Ed). 1994. Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer-Verlag
[63] Ahlqvist O, Keukeaar J, Qukbir K. Rough Classification and Accuracy Assessment. International Journal of Geographical Information Science, 2000, 14(5): 475-479.
[64] Zadeh L A. Fuzzy Sets. Information and Control, 1965.
[65] Shi W Z. Modeling Positional and Thematic Uncertainties in Integratio of Remote Sensing And Geographic information Systems. Enschede: ITC Publication, 1994
[66] 张景雄,杜道生,孙家柄.用随机模拟方法建立矢量数据的误差模型.武汉测绘科技大学学报,2000,25(1):49～54
[67] Burrough P A, Frank A U. Geographic Objects with Indaterminate Bounaries. Basingstoke: Taylor and Francis, 1996
[68] Canters F. Evaluating the Uncertainty of Area Estimates Derived from Fuzzy Land_cover Classification. Photogrammetric Engineering & Remote Sening, 1997, 63(4): 403-414
[69] Vazirgiannis M, Halkidi M. Uncertainty Handling in the Data Mining Process with Fuzzy Logic. IEEE, 2000, 393-398.
[70] 全斌,马智民.GIS中空间数据属性不确定性研究.西安工程学院学报,1998,20(1):67～71
[71] 王新洲,王树良.土地评价中的模糊聚类分析.武汉大学学报(自然科学版),1999(模糊理论与工程专刊):103～107
[72] 王新洲,王树良.模糊综合法在土地定级中的应用[J].武汉测绘科技大学学报,1997,22(1):42～46
[73] 史文中.空间误差处理的理论和方法.北京:科学出版社,1998
[74] Quinlan J. Introduction of Decision Trees. Machine Learning, 1986, (5): 239-266
[75] Ester M, Kriegel H P, Sander J. Spatial Data Mining: a Database Approach. In: scholl M V, eds. Processings of the 5th International Symposium on Spatial Database (SSD'97). Berlin: Springer-Verlag, 1997
[76] Koperski K, Han J. Discovery of Spatial Association Rules in Geographic Information Databases. In: egenhofer M J, Herring J R, Portland M E, eds. Proceedings of the 4th International Symposium on Spatial Databases (SSD'95). Berlin: Springer-Verlag, 1995
[77] Marsala C, Bigolin N M. Spatial Data Mining with Fuzzy Decision Trees. In: Ebecken N F F, eds. Data Mining. Ashurst Lodge: WIT Press/Computational Mechanics Publications, 1998. 235-248
[78] Fayyad U M. Advances in Knowledge Discovery and Data Mining. Menlopark CA: AAAI/ MIT Press, 1996.
[79] Brus D J. Using Norprobability Samples in Design-based Estimation of Spatial Means of Soil Properties. Proceedings Accuracy 2000, Amsterdam, 2000
[80] Yang J B, Madan G, Singh. An Evidential Reasoning Approach for Multiple-Attribute Decision Marking with Uncertainty. IEEE Transactions on System, Man, and Cybernetics, 1994, 24(1): 1-18
[81] Lipski, W J. On Databases with Incomplete information. Journal of ACM, 1981, 28: 41-70.
[82] Chrisman N C. Exploring Geographic Information Systems. New York: John Wiley & Sons, 1997
[83] 骆剑承,周成虎,马江洪.遥感影像特征发现的稳健统计模型研究.中国图像图形学报,1999,4[A](11):952～956.
[84] Data Mining Tool Decision Master. http://www.ibai solution.de.
[85] Eklund, P. W., You, J., Deer, E, 2000. Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, pp. 265-273.
[86] Perner, P., 2000. Mining knowledge in medical image data bases. Dasarathy In: , B. V. (Eds.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Proceedings of the SPIE, Vol. 4057. SPIE, Bellingham, Washington, pp. 359-369.
[87] Zaiane, O. R., Han, J., 2000. Discovery spatial associations in image. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, pp. 138-148.
[88] Zaiane, O. R., Han, J., 2000. Discovery spatial associations in image. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, p. 138-148
[89] Wolf-Fritz Riekert, Faw Uim, Siemens AG Munich. Architecture of a Knowledge-based System For Remote Sensor Data Analysis. ACM, 1990, p238-232.
[90] KoperskiK, Adhihary J, Hart J. Spatial Data Mining: Progress and Challenges. SIGMOD'96 Workshopon Research Issueson Data Mining and Knowledge Dis-covery(DMKD'96). Canada: Montreal, 1996.
[91] Rie Honda, Yuichi Iijima, Tokio Kikuchi, Knowledge Discovery Support System for a Large-scale Satellite Image Data
[92] Y Rui, T S Huang et al. Image retrieval: Past. Present and future[C]. In: Invited Paper in Int SymPosium on Multimedia Information Processing, Taipei, Taiwan, 1997-12: 11-13
[93] 杨凯,卢健,孙家柄等.遥感图像处理原理和方法[M].测绘出版社,1988.
[94] 余旭初.遥感影像分析中智能与可视化技术的联合研究[D].中国人民解放军测绘学院博士学位论文,1997.
[95] 贾永红.计算机图像处理与分析[M].武汉大学出版社,2001
[96] Sonka, M., Hlavac, V., and Boyle, R., 1993, Image processing, analysis and machine vision, Chapman & Hall.
[97] Max Egenhofer and Robert Franzosa. Point-Set Topological Spatial Relations. International Journal of Geographical Information Systems 5 (2): 161-174, 1991.
[98] D. Peuquet and C.-X. Zhan, "An Algorithm to Determine the Directional Relationship Between Arbitrarily-Shaped Polygons in the Plane," Pattern Recognition, vol. 20, no. 1, pp. 65-74, 1987.
[99] Andrew U. Frank Qualitative Spatial Reasoning: Cardinal Directions as an Example, International Journal of Geographical Information Science
[100] 贾永红、李德仁.基于BAYES融合法的多源遥感影像分类[J].武汉测绘科技大学学报.Vol(22)No.3,1997,7.p248-251.
[101] 边肇祺、张学工等.模式识别[M].清华大学出版社,2000
[102] 方勇.遥感图像分析中的多源数据融合技术研究[D].解放军测绘学院硕士论文,1998.6
[103] George Brannon Smith, Susan M. Bridges. FUZZY SPATIAL DATA MINING
[104] Maselli, E, et al., 1995, Estmation of forest parameters through fuzy classfication of TM data. IEEE T-G & RS, Vol. 33, No. 1, pp. 77-84
[105] Wang, F., 1990, Improving remote sensing image analysis through fuzzy information representstion, PE&RS, Vol. 56, No. 8, pp. 1163-1169.
[106] 高新波、李洁.模糊C均值聚类算法中参数m的优选[J].模式识别与人工智能.Vol(13).No.1,2000,3.
[107] Wang, F., 1990, Fuzzy supervised classficition of remote senning images, IEEE T-G&RS, Vol. 28. 2, pp. 194-202.
[108] Wang, F.,1990, Improving remote sening image analysis through fuzzy information representation, PE&RS, Vol. 56. No 8, pp. 1163-1169.
[109] 王旭红 周明全 耿国华,“Dempster-Shafer"证据理论在遥感智能化遥感分类中应用研究,计算机应用与软件,2004.9.
[110] 吴泉源,刘江宁,等.人工智能与专家系统[M] 国防科技大学出版社,1995
[111] 武波,马玉祥.专家系统[M].北京理工大学出版社,2001。
[112] Theresa Beaubouef etc. Fuzzy Rough Set Technique for Uncertainty Processing in Relational Database[J]. International Joumai of Intelligent System, 2000(5), 23-27.
[113] M. E. Yahia, R. Mahmod etc. Rough Neural Expert System[J]. Expert System with application, 2000, 1845—51
[114] 李凡.专家系统中的不确定性[M]气象出版社,1992
[115] Shafer G. A Mathematical Theory of Evidence[M]. Princeton University Press, Princeton, 1976
[116] 王万森.人工智能原理及其应用[M].电子工业出版社.142—153
[117] Shafer G. A Mathematical Theory of Evidence. Princeton University Press, Princeton, 1976
[118] 吴泉源,刘江宁,等.人工智能与专家系统[M] 国防科技大学出版社,1995
[119] 牟克典等.时态Dempster_Shaper理论[J].计算机科学.2003年 VOL.30.NO.7.4-6
[120] P. Gong. Integrated Analysis of Spatial Data from Multiple Sources: Using Evidentential Reasoning and Artificial Neural Network Techniques for Geological Mapping. Photogrammetric Engineering & Remote Sening, Vol. 62, No. 5, May 1996, pp. 513-523.
[121] 杜文吉,谢维信.基于证据理论的模式识别算法[J].西安电子科技大学学报.1999,Vol.26 No.4,p533-537.
[122] 武波,马玉祥.专家系统[M].北京理工大学出版社,2001
[123] 史忠植.高级人工智能[M].科学出版社,1998.
[124] Perner, P., Zscherpel, U., Jacobsen, C., 2001. A comparison between neural networks and decision trees based on data from industrial radiographic testing. Pattern Recognition Letters 2, 47-54.
[125] Mengjie Zhang. Neural Networks for Multiclass Object Mining. Computer Science and Technology. May 19-21, 2003 Cancum, Mexico, 214-218
[126] 张乃尧等.神经网络与模糊控制[M].清华大学出版社,1998.
[127] 韩力群,人工神经网络理论、设计及应用[M].化学工业出版社,2001年.
[128] 杨广文、王鼎兴、郑纬民等.一种利用确定性退火技术的聚类模型与算法研究[J].软件学报.1999,Vol.10,No.6
[129] 扬朋林、贺新.人工神经网络与遗传算法结合的研究[J].现在电子技术,2002,Vol(143).No.12,p105-108
[130] 施建强、刘晓平.基于遗传算法的数据挖掘技术的研究[J].电脑与信息技术,2003(1)P9-15
[131] 陈玉敏.基于神经网络的遥感影像分类研究[J].测绘信息与工程,2002,7.Vol(27),3.p6-8.
[132] C Breen, L Khan, A Kumar et al. Ontology-based Image Classification Using Neural Networks[C]. In: Proc of SPIE, Boston, MD, 2002—07
[133] 张乃尧等.神经网络与模糊控制[M].清华大学出版社,1998.
[134] 余英林、李海洲.神经网络与信息分析[M].华南理工大学出版社,1996.
[135] 李戈等.基于神经元网络聚类的研究[J].青岛大学学报,2001,16(4):1-4
[136] Bassel Solaiman etc. An Information Fusion Method for Multispectral Image Classification Postprocessing[J]IEEE TRANS. On GEOSCIENCE AND REMOTE SENSING, 1998, 36(2).
[137] 李海君.SOM网络在图像分割中的应用[J].计算技术与自动化,Vol.19,No.3,2000.9.p21-24
[138] 沈培华.支持矢量机算法研究与应用[D].南京理工大学,2002
[139] 张建沛.支持矢量机在数据挖掘中的应用[D].哈尔滨工程大学硕士论文,2002
[140] 张学工.关于统计学习与支持向量机[J].自动化学报,2000年第26卷,第一期,32-42
[141] 萧嵘、姚穗、范劲松.一种新的机器学习算法:Support Vector Machines[J].模式识别与人工智能,Vol.13,No.3,285-289
[142] C. J. C. Burges, "A tutorial on support Vector Machines for Pattern Recognition" Data Mining and Knoelwdge Discovery, 2(2): 121-167, 1998.
[143] J. A. K. Suykens, L. Lukas, and J. Vandewalle. Sparse least squares support vector machine classifiers. In ESANN'2000 Eurpean Symposium on Artificial Neural Networks pages 37-42, 2000.
[144] 骆剑承、周成虎.支持向量机及其遥感影像空间特征提取和分类的应用研究[J].遥感学报.2002,Vol(6).No.1
[145] Fabio Roll, Giorgio Fumera. Support Vector Machines for Remote-sening Image Classification. SPIE Processing on Image an Signal Processing for Sening Ⅵ, Sebasticano B. Serpico
[146] Weston J, Watkins C. Mulit-class Support Vector Machines. Roaly Holloway College, Tech Rep: CSD-TR-98-04, 1998
[147] 李雄飞、宋海玉、谢忠时.图像数据挖掘模型与方法[J].吉林大学学报,Vol.32.No 1,2002 p90-92
[148] R. Agrawel, et al. Database mining: A performance perspective. IEEE transactions on Knowledge and data engineering, 1993, 5(6), 914-925
[149] S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to correlations. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of data(SIGMOD'97) 265-276, Tucson, AZ, May, 1997.
[150] 陆丽娜,陈亚萍,魏恒义等,挖掘关联规则中Apriori算法研究,小型微型计算机系统,第21卷,第九期,940～942
[151] K.-L. Ong, W. -K. Ng, and E.-P. Lira. Mining Multi-Level Rules with Recurrent Items Using FP'-Tree. In Proc. of ICICS, Singapore, Oct. 2001. http://citeseer.nj.nec.com/ong01mining.html
[152] 袁红春、熊范纶、杭小树等.一个适用于地理信息系统的数据挖掘工具—GISMiner[J]中国科学技术大学学报.Vol.32.No.2,2002,4.p217-224.
[153] 张群洪.基于J2EE的空间关联规则挖掘理论研究与原型系统实现[D].福州大学硕士研究生学位论文,2004
[154] Yodar Barbara J, Rita E Pettigrew-Crosby. Predicting nitrogen and chlorophy Ⅱ content and concentration from reflectance spectra (400-2500nm)at leaf and canopy scales[J]. Remote Sens. Environ, 1995. 53: 199-211
[155] R. Agrawal, et al. Mining association rules between sets of items in large database. Proc ACM SigMOD int'l conf. Management of data, Washington, DC, May 1993, 207-216
[156] D. W. cheung, et al. Maintenance of discover association rules in large databases: an Incremental on data engineering, New Oreleans Louisiana, 1995, 106-114.
[157] J. Han, Y. Fu. Discovery of Multi-level association rules. Proc. of 21st international conference on very large databases, Zurich, Switzerland, Sept. 1995, 407-419.
[158] R. Srikant. R. agrawal. Mining generalized association rules. Proc. of 21st international conference on very large database, Zurich, Switzerland, Sept. 1995, 407-419.
[159] G. piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In: G. piatetsky Shapiro and W. J. Frawley eds. Knoeledge discovery in database. AAAI/MITpress. 1991, 229-248.
[160] R. Srikant, R. Agrawal. Mining association rules with item constrains. Proc. of the 3rd int'l conf. On knowledge discovery in database and database and mining, Newport Beach, California, August, 1997, 76-73.
[161] R. Ng, L. v. S. Lakshmanan, J. Han and A, Pang. Exploratory mining and pruning optimization of constrainted associations rules, Proc. of 1998 Acm SIGMOD conf, On management of data, Seattle, Washington, June, 1998, 13-24
[162] Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational and object-oriented database(FDOOD'95), Singapore, December 1995, 39-46.
[163] 舒风笛、毋国庆、王敏.图像关联规则挖掘[J].小型微机计算机系统.Vol.22.No.11,2001,11.p1353-1356.
[164] Jelena Tesic, Shawn Newsam and B. S. Manjunath. Mining Images Databases Using Perceptual Assocoation Rules, 2003.
[165] Qin Ding, Qiang Ding, and Willianm Perrizo, "Association rule mining on remotely sensed images using P-trees" in Proc. PAKDD, 2002.
[166] C Ordonez, E Omiecinskj. Discovering Association Rules Based on Image Content[C]. In: Proceedings of the IEEE Advances in Digltal Libraries Conference, Baltimore, MD, 1999-05: 38-49
[167] Osmar R Zajane, J Han. Finding Spatial Associations in images[C].In: SPIE 14th Int Symposium, Orlando, FL, 2000-04: 138-147
[168] Ishwar K Sethj et al. Mining Association Rules between Low_level Image Features and High-Level Concepts. 2000.
[169] Osmar R Zaime et al. Mammography Classfication By An Association Rule Based Classifier[C]. In: Proc MDM/KDD2002、Canada, 2002, 07
[170] William Perrizo, Qin Ding, Qiang Ding, Amalendu Roy, On Mining Satellite and Other Remotely Sensed Images. http://www.cs.cornell.edu/johannes/papers/dmkd2001-papers/p7_ding.pdf
[171] Koperski, K. and J. Han. 1995. "Discovery of spatial association rules in geographic information databases." In Adv. in Spatial Databases: Proc. of the 4th International Symp. on Large Spatial Databases, Portland, Maine, August 6-9, 1995. pp. 47-66, Springer.
[172] Osmar R. Z aiane, Maria-Luiza Antonie and Alexandru Coman. Mammography Classification by Association Rule-based Classifier[C]. MDM / KDD2002: International Workshop on Multimedia Data Mining (with ACM SIGKDD 2002).
[173] Liu B, Hsu W, Ma Y, 'Integrating Classification and Association Rule Mining.' Proc. 4th Int. Conf. KD. and DM. (KDD-98, Plenary Presentation), New York, USA, 1998.
[174] Wynne Hsu, Mong Li Lee, Kheng Guan Goh. Image Mining in IRIS: Integrated Retinal Information System. http://www.comp.nus.edu.sg/～leeml/papers
[175] 铁治欣,陈奇等,关联规则采掘综述,计算机应用研究,2000,第一期,P1-6.
[176] 潘永生、庄天戈.决策数算法及其在乳腺疾病图像数据挖掘中的应用[J].计算机应用研究,2002,p78-81
[177] 张彤、潘和平.决策树的形式算法及其在地理信息学中的应用[J].测绘通报,2002,7,p51-54
[178] 朱绍文、胡宏银、王泉德.决策树采掘技术及发展趋势[J].计算机工程,Vol.26 No.10 p1-4.
[179] 牟凤云,朱博勤,贺华中.基于多特征的高分辨率遥感图像分析.会议论文集
[180] 曲文龙、李卫东、杨炳懦.图像挖掘技术研究[J].计算机工程与应用.2004.5,p1-4.
[181] William I Grosky et al. An Image Data Model. Lecture Notes in Computer Science, 2000: 14—25
[182] Santosh Kulkami et al. Vector Space Image ModeI(VSIM) for Content-based Image Retrieval. 1999.
[183] Ana B. Benite2 et al. ObJect—based multimedia content description schemes and applications for MPEG—7[J]. Signal Processing: Image Communication, 2000.
[184] B. S. Manjunath, P. Salembier, and T. sikora, Eds., introduction to MPEG7: Multimedia Content Description Interface, John Wiley & Sons, 2000.
[185] ALZIR FELIPPE B. ANTUNES, CHRISTEL LINGNAU, JORGE CENTENO DA SILVA. OBJECT ORIENTED ANALYSIS AND SEMANTIC NETWORK FOR HIGH RESOLUTION IMAGE CLASSIFICATION.[J] Anais XI SBSR, Belo Horizonte, Brasil, 05-10 april 2003, INPE, p. 273-279.
[186] eCognition, 2001. eCognition User Guide. Definjens Imaging, Munchen, Germany.
[187] BAATZ, M. A. S. (2000). Multiresolution segmentation-an optimization approach for high quality multi-scaleimage segmentation. Angewandte Geographische Informationsverarbeitung XI. England. 282pp.
[188] 田捷、包尚联、周明全.医学影像处理与分析[M].电子工业出版社.2003.9,p37.
[189] 赫荣威译.计算机图像处理技术[M].科学出版社.1999.
[190] James C. Tilton、Giovanni Marchisio、KrzysztofKoperski and Mihai Datcu, Image Information Mining Utilizing Hierarchical Segmentation. http://www.washjeff.edu/german/imagemining
[191] Gao Yan. Pixel based and object oriented image analysis for coal fire research. http://www.definins.com.eCognition Application.2003.
[192] G. Willhauck, U. Benz, F. Siegert. SEMIAUTOMATIC CLASSIFICATION PROCED URES FOR FIRE MONITORING USING MULTITEMPORAL SAR IMAGES AND NOAAAVHRR HOTSPOT DATA. http://www.definins.com.eCognition Application.2002.
[193] Wong T. H., S. B. Mansor, M. R. Mispan, N. Ahmad, W. N. A. Sulaiman, Feature Extraction based on Objected oriented Analysis. http://www.definins.com.eCognition Application. 2003.
[194] 王宇宙.计算机视觉三维重建理论与应用[D].西北大学博士学位论文,2004
[195] 傅明.基于Web的空间数据挖掘研究[D].中南大学博士学位论文,2004.
[196] Krzysztof Koperski, Giovanni B. Marchisio. Multi-level Indexing and GIS Enhanced Learning for Satellite Imageries. Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining August 20-23, 2000, Boston, MA, USA.
[197] Patricia G. Foschi, Feature Extraction for Image Mining, www.pubtic.asu.edu/～nkolipp/geria.html.
[198] G. Wyszecki and W. S. Stiles. Color Science: Concept and Methods. 1982.
[199] Swain M J. Ballard D H. Color Indexing, Int. J. of computer Vision, 1992, 13(8): 599～605
[200] Krzysztof Koperski, Giovanni B. Marchisio, and Karrie Meyer. Information Fusion and Mining from Satellite Imagery and GIS data. http:gis.esri.com/libry/userconf/proc01/professional/papers/pap271/p271.htm
[201] Fountain, S. R., T. N. Tan, K. D. Baker. A Comparative Study of Rotation Invariant Classification and Retrieval of Texture Images. In On-Line Proceedings of the Ninth British Machine Vision Conference 1998. http://www.bmva.ac.uk/bmvc/1998/index.htm.
[202] Hayley, G. M., and B. M. Manjunath, Rotation Invariant Texture Classification using Modified Gabor Filters, In Proc. of IEEE ICIP95, pp. 262-265, 1994.
[203] C. Lee & D. A. Landgrebe, "Decision Boundary Feature Selection for Non-Para metric Classifiers," in Proc. SPSE's 44th Annual Conference, pp. 475-478, 1991.
[204] C. Lee & D. A. Landgrebe, "Feature Extraction and Classification Algorithms for High Dimensional Data," PhD Dissertation in School of Electrical Engineering, Purdue University, West Lafayette, Indiana, 1992.
[205] David Landgrebe. Multispectral Data Analysis: A Signal Theory Perspective.http://dynamo.ecn.purdue.edu/～biehl/MultiSpec/
[206] David Landgrebe. Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data
[207] David Landgrebe and Larry Biehl. An Introduction To MultiSpec. http://dynamo.ecn.purdue.edu/～biehl/MultiSpec
[208] David Landgrebe. On Information Extraction Principles for Hyperspectral Data. http://dynamo.ecn.purdue.edu/～biehl/MultiSpec
[209] 段立娟,高文,林守勋,马继涌 图像检索中的动态相似性度量方法,计算机学报,2001,24(11):1256～1261
[210] 谢昆青、马修军、杨冬青等译.空间数据库[M].机械工业出版社,2003。
[211] Han, J., Y. Fu, W. Wang, K. Koperski, and O. R. Zanne. DMQL: A Data Mining Query Language for Relational Databases. In Proc. of the Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, QB, pp. 27-34, 1996.
[212] Wolf-Fritz Riekert, FAW Ulm, Siemens AG Munich. Architecture of a Knowledge-based System for Remote Sensor Data Analysis. ACM, 1990. pp. 372-378.
[213] 姜宁,吴桂清,罗文钗.一种基于数据挖掘思想的专家系统推理机制[J].湖南大学学报.Vol 29,3,7,2002.
[214] 陈莉,焦李成.Internet/Web数据挖掘研究现状及最新进展[J].西安电子科技大学学报(自然科学版),2001,Vol(28),No.1,p114-119.
[215] 朱琳玲,胡学钢,穆斌.基于WEB的数据挖掘研究综述[J].电脑与信息技术.2002(6).p45-48.
[216] RaymondKosala, HendrikBlockeel. WebMiningRe-search: A Survey. July2000.
[217] 张娥,冯耕中,战子玉.Web数据应用的利器—Web数据挖掘[J].情报学报.vol.21,2002,12.p686-690.
[218] Mike Perkowitz and Oren Etzioni. Adaptive Web Sites: Automatically Synthesizing Web Pages. In Proceedings of Fifeenth National Conference on Artificial Intelligence, Madision, WI, 1997
[219] J. han, O. R. Zaine, M. Xin. Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. Proc. Advances in Digital Libraries Conf. (ADL'98), Santa Barbara, CA, April 1998, 19-29.
[220] 王海梅 朱世顺 周献中.广域网上基于图像数据挖掘系统设计的探讨[J].兵工学报.Vol.24 No.4 Nov 2003.
[2] 范明、孟小峰等译.数据挖掘[M].机械工业出版社,2001
[3] Mengjie Zhang. Neural Networks for Multiclass Object Mining. Computer Science and Technology. May 19-21, 2003 Cancum, Mexico, 214-218
[4] 李德仁、王树良、李德毅等.论空间数据挖掘和知识发现的理论与方法[J].武汉大学学报.信息科学版.Vol(7).2003年.221-233.
[5] 李德仁.王树良.史文洲等.论空间数据挖掘和知识发现[M].科学出版社,2000.
[6] 黄添强.关于空间数据挖掘环境调控空间决策支持系统研究[D].福州大学硕士学位论文,2003.
[7] 李丙春.基于医学影像的计算机模式分类技术研究与应用[D].西北大学硕士学位论文,2004.
[8] 斐韬、周成虎、骆剑成.空间数据知识发现研究进展评述.
[9] 游代安.GIS辅助的遥感图像处理与分析技术的研究[D].中国人民解放军测绘学院学位论文,2001
[10] 李德仁、关泽群.空间信息系统集成与实现[M].武汉大学出版社,2002
[11] 承继成等.数字地球导论[M].科学出版社,2000.
[12] 杨崇俊.“数字地球”周年综述[J].测绘软科学研究.1999.5(3)
[13] 陈述彭,鲁学军,周成虎.地理信息系统导论[M].科学出版社,2000
[14] 朱述龙、张占睦.遥感图像获取与分析[M].科学出版社,2000
[15] 周成虎等.遥感影像地学理解与分析[M].科学出版社,1999.
[16] Jiawei Han and Micheline Kamber. Data Mining, Concepts and Techniques. Morgan Kaufmann, 2001.
[17] M. Antonie, O. Zaiane, and A. Coman. Application of data mining techniques for medical image classification. In Proceedings of Second International Workshop for Multimedia Data Mining (MDM/KDD'2001) in conjunction with ACM SIGKDD conference, pages 94-101, 2001.
[18] Y. Rui, T. S. Huang, and S. Chang. Image retrieval: Current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10(4): 39-62, 1999. Http//www.esa.asu.edu
[19] Active learning for classifying a spectrally variable subject-(Dr. Patricia G. Foschi and Dr. Huan Liu), 2002. In Proceedings of the 2nd Pattern Recognition for Remote Sensing Workshop (PRRS2002), 16 August 2002, Niagara Falls, Canada, pp. 115-124.
[20] Class-Specific Ensembles for Active Learning Digital Imagery-Amit Mandvikar, and Huan Liu. Accepted as a student paper for the SIAM International Conference on Data Mining. Florida, 2004.
[21] 马超飞.基于关联规则的遥感数据挖掘与应用[D].中国科学院博士研究生学位论文,2002.
[22] 张建沛.支持向量机在数据挖掘中的应用[D].哈尔滨工程大学硕士研究生学位论文,2002.
[23] 王旭红、周明全、耿国华.遥感影像信息挖掘的框架研究[J].计算机应用与软件.(已收录)
[24] Ji Zhang, Wyrme Hsu, Mong Li Lee. Image Mining: Trends and Developments. 2001, http://www.comp.nus.edu.sg/～whsu/publication.
[25] Zhang Ji, Hsu, Mong, Lee, Image Mining: Issues, Frameworks And Techniques, Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), in conjunction with ACM SIGKDD conference. San Francisco, USA, August 26, 2001.
[26] Petra Perner. Image mining: issues, framework, a generic tool and its application to medical-image diagnosis[J]. Engineering Applications of Artificial Intelligence 15(2002) 205-216 http://www.elsevise.com/locate/engappai
[27] Ji Zhang, Wynne Hsu, Mong Li Lee, An Information-driven Framework for Image Mining. 2001, http://www.elsevise.com/locate/engappai
[28] Perner, P., 1998. Case-based reasoning for the low-level and high-level unit of an image interpretation system. In: Singh, S. (Ed.), Advances in Pattern Recognition. Springer, Berlin, pp. 45-54.
[29] 邸凯昌,李德仁,李德毅.空间数据挖掘和知识发现的框架[J].武汉测绘科技大学学报,1997,2(4):328～332.
[30] 邸凯昌.空间数据挖掘和知识发现的理论与方法[D].武汉:武汉测绘科技大学,1999.
[31] J. S. D. Bonet. Image Preprocessing for Rapid Selection in "Pay attention mode". MIL, 2000.
[32] M. C. Burl et al. Mining for Image Content. In Systemics, Cybernetics, and Information Systems: Analysis and Synthesis, (Orlando, FL), July 1999.
[33] S. Gibson et al. Intelligent Mining in image Databases, with Applications to Satellite Imaging and Web Search, Data Mining and Computational Intelligence, Springer-Verlag Berlin, 2001.
[34] R. Kazman and J. Kominek. Information Organization in Multimedia Resources. In proceedings of the 11th annual international conference on Systems documentation, Pages 149-162, 1993.
[35] Y. Rui, T. S. Huang et al. Image Retrieval: Past, Present and Future. Invited paper in Int Symposium on Multimedia Information Processing, Taipei, Taiwan, Dec 11-13, 1997.
[36] M. Flickner, H. S. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, P. Yanker. Query by Image and Video Content: The QBIC system, in IEEE Computer Volume 28 No. 9, pp 23-32, 1995.
[37] J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. Jain, C-F. Shu. Virage Image Search Engine: An Open Framework for Image Management, in Storage and Retrieval for Image and Video Databases (SPIE), pp 76-87, 1996.
[38] J. Feder. Towards Image Content-based Retrieval for the World-Wide Web", Advanced Imaging 11(1), 26-29, 1996.
[39] A. Pentland, R. Picard, and S. Sclaroff. Photobook: Tools for Content-Based Manipulation of Image Databases. SPIE Storage and Retrieval of Image & Video Databases Ⅱ, Feb 1994
[40] V. E. Ogle, and M. Stonebraker. Chabot: Retrieval from a Relational Database of Images, in IEEE Computer Volume 28 No. 9, pp 40-48, 1995.
[41] J. R. Smith and S. F. Chang. VisualSEEk: A Fully Automated Content-based Image Query System, in Proceedings of the 4th ACM International Multimedia Conference and Exhibition, Boston, MA, USA, 11. 1996.
[42] S. Mehrotra, K. Chakrabarti, M. Ortega, Y, Rui, and T. S. Huang. Towards Extending Information Retrieval Techniques fro Multimedia Retrieval. In 3rd International Workshop on Multimedia Information Systems, Como, Italy, 1997.
[43] C. Nastar, M. Mitschke, C. Meilhac, and N. Boujemaa. Surfimage: a Flexible Content-based Image Retrieval System. In ACM-Multimedia, Bristol, England, September 12-16 1998.
[44] R. Manmatha and S. Ravela. A Syntactic Characterization of Appearance and its Application to Image Retrieval. In Proceedings of the SPIE conference on Human Vision and Electronic Imaging Ⅱ, Vol, 3016, San Jose, CA, Feb. '97, 1997.
[45] N. Katayama and S. Satoh. The SR-tree: An Index Structure for High-dimensional Nearest Neighbor Queries. In proceedings of the 1997 ACM SIGMOD Conference, pages 369-380, Tucson, Arizona, May 1997.
[46] K. Lin, H. V. Jagadish and C. Faloutsos. The TV-tree: An Index Structure for High-Dimensional Data. The VLDB Journal, 3 (4): 517-542, 1994.
[47] S. Berchtold, D. A. Keim and H. P. Kriegel. The X-tree: An Index Structure for High-dimensional Data. In Proceedings of the 22nd VLDB Conference, pages 28-39, Mumbai, India, September 1996.
[48] B. C. Ooi, K. L. Tan, C. Yu and S. Bressan. Indexing the Edges-A Simple and Yet Efficient Approach to High-Dimensional Indexing, 19th ACMSIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 166-174, Dallas, Texas, May 2000.
[49] Y. Rui, T. S. Huang et al. Image Retrieval: Past, Present and Future. Invited paper in Int Symposium on Multimedia Information Processing, Taipei, Taiwan, Dec 11-13, 1997.
[50] A. Guttman. R-tree: A Dynamic Index Structure for Spatial Searching. ACM SIGMOD, 1984.
[51] S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
[52] L. Bruzzone and D. E Prieto. Unsupervised Retraining of a Maximum Likelihood Classifier for the Analysis of Multitemporal Remote Sensing Images, IEEE Transactions on Geoscience andRemote Sensing, Volume: 39 Issue: 2, pp 456-460, Feb. 2001.
[53] O. R. Zaiane, J. W. Han et al. Mining MultiMedia Data. CASCON'98: Meeting of Minds, pp 83-96, Toronto, Canada, November 1998.
[54] J. Z. Wang, J. Li et al. System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms. Interactive Distributed Multimedia Systems and Telecommunication Services, Proceedings of the Fourth European Workshop (IDMS'97), 1997.
[55] T. Sellis, N. Roussopoulos and C. Faloutsos. The R+ Tree: A Dynamic Index for Multi-dimensional Objects. In Proc 12th VLDB, 1987
[56] A. Kitamoto. Data Mining for Typhoon Image Collection. In Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), San Francisco, CA, USA, August, 2001
[57] A. K. Jain, M. N. Murty and P. J. Flynn. Data Clustering: A Review. ACM computing survey, Vol. 31, No. 3, September 1999.
[58] 郭达志,胡召玲,陈云浩.GIS中空间对像的不确定性研究.中国矿业大学学报(自然科学版),2000,29(1):20～24.
[59] Lee Es. Neuro_fuzzy Estimation in Spatial Statistics. Journal of Mathematical Analysis and Applications, 2000, 249: 221～231.
[60] 周海燕,王家耀,吴升.空间数据挖掘技术及其应用[J].测绘通报,2002,11(2)11～13.
[61] Pawlak Z. 1982. Rough Sets. Int. Joumal of Computer and Information Sciences, 11 (5)
[62] Ziarko W (Ed). 1994. Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer-Verlag
[63] Ahlqvist O, Keukeaar J, Qukbir K. Rough Classification and Accuracy Assessment. International Journal of Geographical Information Science, 2000, 14(5): 475-479.
[64] Zadeh L A. Fuzzy Sets. Information and Control, 1965.
[65] Shi W Z. Modeling Positional and Thematic Uncertainties in Integratio of Remote Sensing And Geographic information Systems. Enschede: ITC Publication, 1994
[66] 张景雄,杜道生,孙家柄.用随机模拟方法建立矢量数据的误差模型.武汉测绘科技大学学报,2000,25(1):49～54
[67] Burrough P A, Frank A U. Geographic Objects with Indaterminate Bounaries. Basingstoke: Taylor and Francis, 1996
[68] Canters F. Evaluating the Uncertainty of Area Estimates Derived from Fuzzy Land_cover Classification. Photogrammetric Engineering & Remote Sening, 1997, 63(4): 403-414
[69] Vazirgiannis M, Halkidi M. Uncertainty Handling in the Data Mining Process with Fuzzy Logic. IEEE, 2000, 393-398.
[70] 全斌,马智民.GIS中空间数据属性不确定性研究.西安工程学院学报,1998,20(1):67～71
[71] 王新洲,王树良.土地评价中的模糊聚类分析.武汉大学学报(自然科学版),1999(模糊理论与工程专刊):103～107
[72] 王新洲,王树良.模糊综合法在土地定级中的应用[J].武汉测绘科技大学学报,1997,22(1):42～46
[73] 史文中.空间误差处理的理论和方法.北京:科学出版社,1998
[74] Quinlan J. Introduction of Decision Trees. Machine Learning, 1986, (5): 239-266
[75] Ester M, Kriegel H P, Sander J. Spatial Data Mining: a Database Approach. In: scholl M V, eds. Processings of the 5th International Symposium on Spatial Database (SSD'97). Berlin: Springer-Verlag, 1997
[76] Koperski K, Han J. Discovery of Spatial Association Rules in Geographic Information Databases. In: egenhofer M J, Herring J R, Portland M E, eds. Proceedings of the 4th International Symposium on Spatial Databases (SSD'95). Berlin: Springer-Verlag, 1995
[77] Marsala C, Bigolin N M. Spatial Data Mining with Fuzzy Decision Trees. In: Ebecken N F F, eds. Data Mining. Ashurst Lodge: WIT Press/Computational Mechanics Publications, 1998. 235-248
[78] Fayyad U M. Advances in Knowledge Discovery and Data Mining. Menlopark CA: AAAI/ MIT Press, 1996.
[79] Brus D J. Using Norprobability Samples in Design-based Estimation of Spatial Means of Soil Properties. Proceedings Accuracy 2000, Amsterdam, 2000
[80] Yang J B, Madan G, Singh. An Evidential Reasoning Approach for Multiple-Attribute Decision Marking with Uncertainty. IEEE Transactions on System, Man, and Cybernetics, 1994, 24(1): 1-18
[81] Lipski, W J. On Databases with Incomplete information. Journal of ACM, 1981, 28: 41-70.
[82] Chrisman N C. Exploring Geographic Information Systems. New York: John Wiley & Sons, 1997
[83] 骆剑承,周成虎,马江洪.遥感影像特征发现的稳健统计模型研究.中国图像图形学报,1999,4[A](11):952～956.
[84] Data Mining Tool Decision Master. http://www.ibai solution.de.
[85] Eklund, P. W., You, J., Deer, E, 2000. Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, pp. 265-273.
[86] Perner, P., 2000. Mining knowledge in medical image data bases. Dasarathy In: , B. V. (Eds.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Proceedings of the SPIE, Vol. 4057. SPIE, Bellingham, Washington, pp. 359-369.
[87] Zaiane, O. R., Han, J., 2000. Discovery spatial associations in image. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, pp. 138-148.
[88] Zaiane, O. R., Han, J., 2000. Discovery spatial associations in image. In: Dasarathy, B. V. (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Vol. 4057. SPIE, Bellingham, Washington, p. 138-148
[89] Wolf-Fritz Riekert, Faw Uim, Siemens AG Munich. Architecture of a Knowledge-based System For Remote Sensor Data Analysis. ACM, 1990, p238-232.
[90] KoperskiK, Adhihary J, Hart J. Spatial Data Mining: Progress and Challenges. SIGMOD'96 Workshopon Research Issueson Data Mining and Knowledge Dis-covery(DMKD'96). Canada: Montreal, 1996.
[91] Rie Honda, Yuichi Iijima, Tokio Kikuchi, Knowledge Discovery Support System for a Large-scale Satellite Image Data
[92] Y Rui, T S Huang et al. Image retrieval: Past. Present and future[C]. In: Invited Paper in Int SymPosium on Multimedia Information Processing, Taipei, Taiwan, 1997-12: 11-13
[93] 杨凯,卢健,孙家柄等.遥感图像处理原理和方法[M].测绘出版社,1988.
[94] 余旭初.遥感影像分析中智能与可视化技术的联合研究[D].中国人民解放军测绘学院博士学位论文,1997.
[95] 贾永红.计算机图像处理与分析[M].武汉大学出版社,2001
[96] Sonka, M., Hlavac, V., and Boyle, R., 1993, Image processing, analysis and machine vision, Chapman & Hall.
[97] Max Egenhofer and Robert Franzosa. Point-Set Topological Spatial Relations. International Journal of Geographical Information Systems 5 (2): 161-174, 1991.
[98] D. Peuquet and C.-X. Zhan, "An Algorithm to Determine the Directional Relationship Between Arbitrarily-Shaped Polygons in the Plane," Pattern Recognition, vol. 20, no. 1, pp. 65-74, 1987.
[99] Andrew U. Frank Qualitative Spatial Reasoning: Cardinal Directions as an Example, International Journal of Geographical Information Science
[100] 贾永红、李德仁.基于BAYES融合法的多源遥感影像分类[J].武汉测绘科技大学学报.Vol(22)No.3,1997,7.p248-251.
[101] 边肇祺、张学工等.模式识别[M].清华大学出版社,2000
[102] 方勇.遥感图像分析中的多源数据融合技术研究[D].解放军测绘学院硕士论文,1998.6
[103] George Brannon Smith, Susan M. Bridges. FUZZY SPATIAL DATA MINING
[104] Maselli, E, et al., 1995, Estmation of forest parameters through fuzy classfication of TM data. IEEE T-G & RS, Vol. 33, No. 1, pp. 77-84
[105] Wang, F., 1990, Improving remote sensing image analysis through fuzzy information representstion, PE&RS, Vol. 56, No. 8, pp. 1163-1169.
[106] 高新波、李洁.模糊C均值聚类算法中参数m的优选[J].模式识别与人工智能.Vol(13).No.1,2000,3.
[107] Wang, F., 1990, Fuzzy supervised classficition of remote senning images, IEEE T-G&RS, Vol. 28. 2, pp. 194-202.
[108] Wang, F.,1990, Improving remote sening image analysis through fuzzy information representation, PE&RS, Vol. 56. No 8, pp. 1163-1169.
[109] 王旭红 周明全 耿国华,“Dempster-Shafer"证据理论在遥感智能化遥感分类中应用研究,计算机应用与软件,2004.9.
[110] 吴泉源,刘江宁,等.人工智能与专家系统[M] 国防科技大学出版社,1995
[111] 武波,马玉祥.专家系统[M].北京理工大学出版社,2001。
[112] Theresa Beaubouef etc. Fuzzy Rough Set Technique for Uncertainty Processing in Relational Database[J]. International Joumai of Intelligent System, 2000(5), 23-27.
[113] M. E. Yahia, R. Mahmod etc. Rough Neural Expert System[J]. Expert System with application, 2000, 1845—51
[114] 李凡.专家系统中的不确定性[M]气象出版社,1992
[115] Shafer G. A Mathematical Theory of Evidence[M]. Princeton University Press, Princeton, 1976
[116] 王万森.人工智能原理及其应用[M].电子工业出版社.142—153
[117] Shafer G. A Mathematical Theory of Evidence. Princeton University Press, Princeton, 1976
[118] 吴泉源,刘江宁,等.人工智能与专家系统[M] 国防科技大学出版社,1995
[119] 牟克典等.时态Dempster_Shaper理论[J].计算机科学.2003年 VOL.30.NO.7.4-6
[120] P. Gong. Integrated Analysis of Spatial Data from Multiple Sources: Using Evidentential Reasoning and Artificial Neural Network Techniques for Geological Mapping. Photogrammetric Engineering & Remote Sening, Vol. 62, No. 5, May 1996, pp. 513-523.
[121] 杜文吉,谢维信.基于证据理论的模式识别算法[J].西安电子科技大学学报.1999,Vol.26 No.4,p533-537.
[122] 武波,马玉祥.专家系统[M].北京理工大学出版社,2001
[123] 史忠植.高级人工智能[M].科学出版社,1998.
[124] Perner, P., Zscherpel, U., Jacobsen, C., 2001. A comparison between neural networks and decision trees based on data from industrial radiographic testing. Pattern Recognition Letters 2, 47-54.
[125] Mengjie Zhang. Neural Networks for Multiclass Object Mining. Computer Science and Technology. May 19-21, 2003 Cancum, Mexico, 214-218
[126] 张乃尧等.神经网络与模糊控制[M].清华大学出版社,1998.
[127] 韩力群,人工神经网络理论、设计及应用[M].化学工业出版社,2001年.
[128] 杨广文、王鼎兴、郑纬民等.一种利用确定性退火技术的聚类模型与算法研究[J].软件学报.1999,Vol.10,No.6
[129] 扬朋林、贺新.人工神经网络与遗传算法结合的研究[J].现在电子技术,2002,Vol(143).No.12,p105-108
[130] 施建强、刘晓平.基于遗传算法的数据挖掘技术的研究[J].电脑与信息技术,2003(1)P9-15
[131] 陈玉敏.基于神经网络的遥感影像分类研究[J].测绘信息与工程,2002,7.Vol(27),3.p6-8.
[132] C Breen, L Khan, A Kumar et al. Ontology-based Image Classification Using Neural Networks[C]. In: Proc of SPIE, Boston, MD, 2002—07
[133] 张乃尧等.神经网络与模糊控制[M].清华大学出版社,1998.
[134] 余英林、李海洲.神经网络与信息分析[M].华南理工大学出版社,1996.
[135] 李戈等.基于神经元网络聚类的研究[J].青岛大学学报,2001,16(4):1-4
[136] Bassel Solaiman etc. An Information Fusion Method for Multispectral Image Classification Postprocessing[J]IEEE TRANS. On GEOSCIENCE AND REMOTE SENSING, 1998, 36(2).
[137] 李海君.SOM网络在图像分割中的应用[J].计算技术与自动化,Vol.19,No.3,2000.9.p21-24
[138] 沈培华.支持矢量机算法研究与应用[D].南京理工大学,2002
[139] 张建沛.支持矢量机在数据挖掘中的应用[D].哈尔滨工程大学硕士论文,2002
[140] 张学工.关于统计学习与支持向量机[J].自动化学报,2000年第26卷,第一期,32-42
[141] 萧嵘、姚穗、范劲松.一种新的机器学习算法:Support Vector Machines[J].模式识别与人工智能,Vol.13,No.3,285-289
[142] C. J. C. Burges, "A tutorial on support Vector Machines for Pattern Recognition" Data Mining and Knoelwdge Discovery, 2(2): 121-167, 1998.
[143] J. A. K. Suykens, L. Lukas, and J. Vandewalle. Sparse least squares support vector machine classifiers. In ESANN'2000 Eurpean Symposium on Artificial Neural Networks pages 37-42, 2000.
[144] 骆剑承、周成虎.支持向量机及其遥感影像空间特征提取和分类的应用研究[J].遥感学报.2002,Vol(6).No.1
[145] Fabio Roll, Giorgio Fumera. Support Vector Machines for Remote-sening Image Classification. SPIE Processing on Image an Signal Processing for Sening Ⅵ, Sebasticano B. Serpico
[146] Weston J, Watkins C. Mulit-class Support Vector Machines. Roaly Holloway College, Tech Rep: CSD-TR-98-04, 1998
[147] 李雄飞、宋海玉、谢忠时.图像数据挖掘模型与方法[J].吉林大学学报,Vol.32.No 1,2002 p90-92
[148] R. Agrawel, et al. Database mining: A performance perspective. IEEE transactions on Knowledge and data engineering, 1993, 5(6), 914-925
[149] S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to correlations. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of data(SIGMOD'97) 265-276, Tucson, AZ, May, 1997.
[150] 陆丽娜,陈亚萍,魏恒义等,挖掘关联规则中Apriori算法研究,小型微型计算机系统,第21卷,第九期,940～942
[151] K.-L. Ong, W. -K. Ng, and E.-P. Lira. Mining Multi-Level Rules with Recurrent Items Using FP'-Tree. In Proc. of ICICS, Singapore, Oct. 2001. http://citeseer.nj.nec.com/ong01mining.html
[152] 袁红春、熊范纶、杭小树等.一个适用于地理信息系统的数据挖掘工具—GISMiner[J]中国科学技术大学学报.Vol.32.No.2,2002,4.p217-224.
[153] 张群洪.基于J2EE的空间关联规则挖掘理论研究与原型系统实现[D].福州大学硕士研究生学位论文,2004
[154] Yodar Barbara J, Rita E Pettigrew-Crosby. Predicting nitrogen and chlorophy Ⅱ content and concentration from reflectance spectra (400-2500nm)at leaf and canopy scales[J]. Remote Sens. Environ, 1995. 53: 199-211
[155] R. Agrawal, et al. Mining association rules between sets of items in large database. Proc ACM SigMOD int'l conf. Management of data, Washington, DC, May 1993, 207-216
[156] D. W. cheung, et al. Maintenance of discover association rules in large databases: an Incremental on data engineering, New Oreleans Louisiana, 1995, 106-114.
[157] J. Han, Y. Fu. Discovery of Multi-level association rules. Proc. of 21st international conference on very large databases, Zurich, Switzerland, Sept. 1995, 407-419.
[158] R. Srikant. R. agrawal. Mining generalized association rules. Proc. of 21st international conference on very large database, Zurich, Switzerland, Sept. 1995, 407-419.
[159] G. piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In: G. piatetsky Shapiro and W. J. Frawley eds. Knoeledge discovery in database. AAAI/MITpress. 1991, 229-248.
[160] R. Srikant, R. Agrawal. Mining association rules with item constrains. Proc. of the 3rd int'l conf. On knowledge discovery in database and database and mining, Newport Beach, California, August, 1997, 76-73.
[161] R. Ng, L. v. S. Lakshmanan, J. Han and A, Pang. Exploratory mining and pruning optimization of constrainted associations rules, Proc. of 1998 Acm SIGMOD conf, On management of data, Seattle, Washington, June, 1998, 13-24
[162] Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational and object-oriented database(FDOOD'95), Singapore, December 1995, 39-46.
[163] 舒风笛、毋国庆、王敏.图像关联规则挖掘[J].小型微机计算机系统.Vol.22.No.11,2001,11.p1353-1356.
[164] Jelena Tesic, Shawn Newsam and B. S. Manjunath. Mining Images Databases Using Perceptual Assocoation Rules, 2003.
[165] Qin Ding, Qiang Ding, and Willianm Perrizo, "Association rule mining on remotely sensed images using P-trees" in Proc. PAKDD, 2002.
[166] C Ordonez, E Omiecinskj. Discovering Association Rules Based on Image Content[C]. In: Proceedings of the IEEE Advances in Digltal Libraries Conference, Baltimore, MD, 1999-05: 38-49
[167] Osmar R Zajane, J Han. Finding Spatial Associations in images[C].In: SPIE 14th Int Symposium, Orlando, FL, 2000-04: 138-147
[168] Ishwar K Sethj et al. Mining Association Rules between Low_level Image Features and High-Level Concepts. 2000.
[169] Osmar R Zaime et al. Mammography Classfication By An Association Rule Based Classifier[C]. In: Proc MDM/KDD2002、Canada, 2002, 07
[170] William Perrizo, Qin Ding, Qiang Ding, Amalendu Roy, On Mining Satellite and Other Remotely Sensed Images. http://www.cs.cornell.edu/johannes/papers/dmkd2001-papers/p7_ding.pdf
[171] Koperski, K. and J. Han. 1995. "Discovery of spatial association rules in geographic information databases." In Adv. in Spatial Databases: Proc. of the 4th International Symp. on Large Spatial Databases, Portland, Maine, August 6-9, 1995. pp. 47-66, Springer.
[172] Osmar R. Z aiane, Maria-Luiza Antonie and Alexandru Coman. Mammography Classification by Association Rule-based Classifier[C]. MDM / KDD2002: International Workshop on Multimedia Data Mining (with ACM SIGKDD 2002).
[173] Liu B, Hsu W, Ma Y, 'Integrating Classification and Association Rule Mining.' Proc. 4th Int. Conf. KD. and DM. (KDD-98, Plenary Presentation), New York, USA, 1998.
[174] Wynne Hsu, Mong Li Lee, Kheng Guan Goh. Image Mining in IRIS: Integrated Retinal Information System. http://www.comp.nus.edu.sg/～leeml/papers
[175] 铁治欣,陈奇等,关联规则采掘综述,计算机应用研究,2000,第一期,P1-6.
[176] 潘永生、庄天戈.决策数算法及其在乳腺疾病图像数据挖掘中的应用[J].计算机应用研究,2002,p78-81
[177] 张彤、潘和平.决策树的形式算法及其在地理信息学中的应用[J].测绘通报,2002,7,p51-54
[178] 朱绍文、胡宏银、王泉德.决策树采掘技术及发展趋势[J].计算机工程,Vol.26 No.10 p1-4.
[179] 牟凤云,朱博勤,贺华中.基于多特征的高分辨率遥感图像分析.会议论文集
[180] 曲文龙、李卫东、杨炳懦.图像挖掘技术研究[J].计算机工程与应用.2004.5,p1-4.
[181] William I Grosky et al. An Image Data Model. Lecture Notes in Computer Science, 2000: 14—25
[182] Santosh Kulkami et al. Vector Space Image ModeI(VSIM) for Content-based Image Retrieval. 1999.
[183] Ana B. Benite2 et al. ObJect—based multimedia content description schemes and applications for MPEG—7[J]. Signal Processing: Image Communication, 2000.
[184] B. S. Manjunath, P. Salembier, and T. sikora, Eds., introduction to MPEG7: Multimedia Content Description Interface, John Wiley & Sons, 2000.
[185] ALZIR FELIPPE B. ANTUNES, CHRISTEL LINGNAU, JORGE CENTENO DA SILVA. OBJECT ORIENTED ANALYSIS AND SEMANTIC NETWORK FOR HIGH RESOLUTION IMAGE CLASSIFICATION.[J] Anais XI SBSR, Belo Horizonte, Brasil, 05-10 april 2003, INPE, p. 273-279.
[186] eCognition, 2001. eCognition User Guide. Definjens Imaging, Munchen, Germany.
[187] BAATZ, M. A. S. (2000). Multiresolution segmentation-an optimization approach for high quality multi-scaleimage segmentation. Angewandte Geographische Informationsverarbeitung XI. England. 282pp.
[188] 田捷、包尚联、周明全.医学影像处理与分析[M].电子工业出版社.2003.9,p37.
[189] 赫荣威译.计算机图像处理技术[M].科学出版社.1999.
[190] James C. Tilton、Giovanni Marchisio、KrzysztofKoperski and Mihai Datcu, Image Information Mining Utilizing Hierarchical Segmentation. http://www.washjeff.edu/german/imagemining
[191] Gao Yan. Pixel based and object oriented image analysis for coal fire research. http://www.definins.com.eCognition Application.2003.
[192] G. Willhauck, U. Benz, F. Siegert. SEMIAUTOMATIC CLASSIFICATION PROCED URES FOR FIRE MONITORING USING MULTITEMPORAL SAR IMAGES AND NOAAAVHRR HOTSPOT DATA. http://www.definins.com.eCognition Application.2002.
[193] Wong T. H., S. B. Mansor, M. R. Mispan, N. Ahmad, W. N. A. Sulaiman, Feature Extraction based on Objected oriented Analysis. http://www.definins.com.eCognition Application. 2003.
[194] 王宇宙.计算机视觉三维重建理论与应用[D].西北大学博士学位论文,2004
[195] 傅明.基于Web的空间数据挖掘研究[D].中南大学博士学位论文,2004.
[196] Krzysztof Koperski, Giovanni B. Marchisio. Multi-level Indexing and GIS Enhanced Learning for Satellite Imageries. Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining August 20-23, 2000, Boston, MA, USA.
[197] Patricia G. Foschi, Feature Extraction for Image Mining, www.pubtic.asu.edu/～nkolipp/geria.html.
[198] G. Wyszecki and W. S. Stiles. Color Science: Concept and Methods. 1982.
[199] Swain M J. Ballard D H. Color Indexing, Int. J. of computer Vision, 1992, 13(8): 599～605
[200] Krzysztof Koperski, Giovanni B. Marchisio, and Karrie Meyer. Information Fusion and Mining from Satellite Imagery and GIS data. http:gis.esri.com/libry/userconf/proc01/professional/papers/pap271/p271.htm
[201] Fountain, S. R., T. N. Tan, K. D. Baker. A Comparative Study of Rotation Invariant Classification and Retrieval of Texture Images. In On-Line Proceedings of the Ninth British Machine Vision Conference 1998. http://www.bmva.ac.uk/bmvc/1998/index.htm.
[202] Hayley, G. M., and B. M. Manjunath, Rotation Invariant Texture Classification using Modified Gabor Filters, In Proc. of IEEE ICIP95, pp. 262-265, 1994.
[203] C. Lee & D. A. Landgrebe, "Decision Boundary Feature Selection for Non-Para metric Classifiers," in Proc. SPSE's 44th Annual Conference, pp. 475-478, 1991.
[204] C. Lee & D. A. Landgrebe, "Feature Extraction and Classification Algorithms for High Dimensional Data," PhD Dissertation in School of Electrical Engineering, Purdue University, West Lafayette, Indiana, 1992.
[205] David Landgrebe. Multispectral Data Analysis: A Signal Theory Perspective.http://dynamo.ecn.purdue.edu/～biehl/MultiSpec/
[206] David Landgrebe. Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data
[207] David Landgrebe and Larry Biehl. An Introduction To MultiSpec. http://dynamo.ecn.purdue.edu/～biehl/MultiSpec
[208] David Landgrebe. On Information Extraction Principles for Hyperspectral Data. http://dynamo.ecn.purdue.edu/～biehl/MultiSpec
[209] 段立娟,高文,林守勋,马继涌 图像检索中的动态相似性度量方法,计算机学报,2001,24(11):1256～1261
[210] 谢昆青、马修军、杨冬青等译.空间数据库[M].机械工业出版社,2003。
[211] Han, J., Y. Fu, W. Wang, K. Koperski, and O. R. Zanne. DMQL: A Data Mining Query Language for Relational Databases. In Proc. of the Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, QB, pp. 27-34, 1996.
[212] Wolf-Fritz Riekert, FAW Ulm, Siemens AG Munich. Architecture of a Knowledge-based System for Remote Sensor Data Analysis. ACM, 1990. pp. 372-378.
[213] 姜宁,吴桂清,罗文钗.一种基于数据挖掘思想的专家系统推理机制[J].湖南大学学报.Vol 29,3,7,2002.
[214] 陈莉,焦李成.Internet/Web数据挖掘研究现状及最新进展[J].西安电子科技大学学报(自然科学版),2001,Vol(28),No.1,p114-119.
[215] 朱琳玲,胡学钢,穆斌.基于WEB的数据挖掘研究综述[J].电脑与信息技术.2002(6).p45-48.
[216] RaymondKosala, HendrikBlockeel. WebMiningRe-search: A Survey. July2000.
[217] 张娥,冯耕中,战子玉.Web数据应用的利器—Web数据挖掘[J].情报学报.vol.21,2002,12.p686-690.
[218] Mike Perkowitz and Oren Etzioni. Adaptive Web Sites: Automatically Synthesizing Web Pages. In Proceedings of Fifeenth National Conference on Artificial Intelligence, Madision, WI, 1997
[219] J. han, O. R. Zaine, M. Xin. Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. Proc. Advances in Digital Libraries Conf. (ADL'98), Santa Barbara, CA, April 1998, 19-29.
[220] 王海梅 朱世顺 周献中.广域网上基于图像数据挖掘系统设计的探讨[J].兵工学报.Vol.24 No.4 Nov 2003.
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