遥感土地利用/土地覆盖变化信息提取的决策树方法
|
摘要
土地利用／土地覆盖变化(LUCC)信息提取方法的研究是LUCC研究的核心內容和关键技术，本文以中分辨率遥感数据为主要数据源，针对不同生态分区和目标特征，发展了分区分层的决策树LUCC信息提取方法，基本实现了变化信息自动发现-提取以及类型表示的自动化流程。
笔者主要进行了以下方面的研究：
1、建立了遥感及地面实测数据支撑的综合数据库；
2、基本实现了LUCC变化信息的自动化提取流程；
3、发展了基于中分辨率遥感数据的变化信息自动发现方法；
4、有效融合了非监督分类结果以及专家知识基础上的决策树分类技术，建立了试验区决策树分类模型；
5、将决策树技术应用于变化信息的提取方法中，以多源多时相遥感及其他辅助数据为基础，构造了变化转化类型的决策树模型；
6、以中国西部石羊河流域和河北内蒙局部地区为试验研究区，进行了分区分层技术和决策树的方法应用，完成试验研究区反映1980年以来2—3个时相遥感影像的土地利用／土地覆盖分类和LUCC专题信息层的结果提取，并用变化转换关系的矩阵表表示，精度评价表明，分类和变化类型的精度均达到80％以上。
实践证明，所采用的方法和技术提高了LUCC信息提取的自动化水平和精度，并可以在更大范围内推广使用。
The study on the method of extracting change information from land use / cover is the kernel content and key technique, this article is based on remote sensing data of the middle resolution as main data source, aims at different zoology district and goal characteristic, develops :he decision-tree method of extracting of LUCC information based on del-imination in different district technology, realizes auto-find and extraction and automatic flow of expression types.
The author mostly carries on the study as follows:
1. Established the synthetic database which are based on remote-sensing data and survey in the field;
2. Realized the extracting flow of LUCC information automatically;
3. Developed methods of auto-find change information based on middle resolution remote-sensing data;
4. Synchronized the outcome of unsupervised classification and decision-tree classification based on expert knowledge efficiently, built the model of decision-tree classification in test areas.
5. The technology of decision-tree is applied to methods of the extracting change information, with the multi-source, multi-time remote-sensing data and other data as the foundation, the decision-tree model of the change transformation type is constructed;
6. Taking the area of Shiyanghe drainage being in the west of China and local area of Hebei and Neimeng as research district, based on methods of multi-layer in different district technology and decis on-tree, has finished and extracted land use /land cover' s classification and LUCC information from remotely sensed data of 2-3 times since 1980, and they are expressed by a matrix of the relationship of change transformation. The precision evaluation shows that the precision of classification and change types exceeds 80%. Examples prove that the adopted method and technology improve the level of autoimmunization and precision of LUCC information extraction, and can be generalized in a larger scope.
笔者主要进行了以下方面的研究：
1、建立了遥感及地面实测数据支撑的综合数据库；
2、基本实现了LUCC变化信息的自动化提取流程；
3、发展了基于中分辨率遥感数据的变化信息自动发现方法；
4、有效融合了非监督分类结果以及专家知识基础上的决策树分类技术，建立了试验区决策树分类模型；
5、将决策树技术应用于变化信息的提取方法中，以多源多时相遥感及其他辅助数据为基础，构造了变化转化类型的决策树模型；
6、以中国西部石羊河流域和河北内蒙局部地区为试验研究区，进行了分区分层技术和决策树的方法应用，完成试验研究区反映1980年以来2—3个时相遥感影像的土地利用／土地覆盖分类和LUCC专题信息层的结果提取，并用变化转换关系的矩阵表表示，精度评价表明，分类和变化类型的精度均达到80％以上。
实践证明，所采用的方法和技术提高了LUCC信息提取的自动化水平和精度，并可以在更大范围内推广使用。
The study on the method of extracting change information from land use / cover is the kernel content and key technique, this article is based on remote sensing data of the middle resolution as main data source, aims at different zoology district and goal characteristic, develops :he decision-tree method of extracting of LUCC information based on del-imination in different district technology, realizes auto-find and extraction and automatic flow of expression types.
The author mostly carries on the study as follows:
1. Established the synthetic database which are based on remote-sensing data and survey in the field;
2. Realized the extracting flow of LUCC information automatically;
3. Developed methods of auto-find change information based on middle resolution remote-sensing data;
4. Synchronized the outcome of unsupervised classification and decision-tree classification based on expert knowledge efficiently, built the model of decision-tree classification in test areas.
5. The technology of decision-tree is applied to methods of the extracting change information, with the multi-source, multi-time remote-sensing data and other data as the foundation, the decision-tree model of the change transformation type is constructed;
6. Taking the area of Shiyanghe drainage being in the west of China and local area of Hebei and Neimeng as research district, based on methods of multi-layer in different district technology and decis on-tree, has finished and extracted land use /land cover' s classification and LUCC information from remotely sensed data of 2-3 times since 1980, and they are expressed by a matrix of the relationship of change transformation. The precision evaluation shows that the precision of classification and change types exceeds 80%. Examples prove that the adopted method and technology improve the level of autoimmunization and precision of LUCC information extraction, and can be generalized in a larger scope.
引文
1. Turner B L, David Skole, et al. Land-use and Land-cover Change Science/Research Plan[J], IGBP Report NO.35 and IHDP Report No.7, 1995.
2. William E R, Willam BM, Turner B L. Modeling land use and land cover as part of global environment change[J], Climatic change, 1994, 28(3): 45-64.
3. John G. Lyon, Ding Yuan, Lunetta, and Chris D. Elvidge. A Change Detection Experiment Using Vegetation Indices[J], PE&RS, 1998:143-150.
4. Tucker C J, Townshend J R G, Goff T E. Africa land cover classification using satellite data[J], Science, 1985, 22(7): 369-375.
5. M.J. Hansen, S. E. Franklin, C.Woudsma M.Peterson. Forest Structure Classification in the North Columbia Mountains Using the Landsat TM Tasseled Cap Wetness Component[J], Canadian Journal of Remote Sensing, 2001, 27(1): 20-31
6. M.H. Tangestani F.Moore. Comparison of Three Principal Component Analysis Techniques to Porphyry Copper Alteration Mapping A Case Study, MEIDUK Area, Meiduk Area, Kerman[J], Iran Canadian Journal of Remote Sensing, 2001, 27(2): 176-181.
7. Peter Scarth, Stuart R. Phinn. Integrating High and Moderate Spatial Resolution Image Data to Estimate Forest Age Structure[J], Canadian Journal of Remote Sensing, 2001, 27(2): 129-141.
8. Lesley-Ann Dupign-Giroux and John E. Lewis Moisture. Index for Surface Characterization over a Semiarid Area[J], PE&RS 1999, 65(8): 937-945.
9. Rick L. Lawrence and Andrea Wright. Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis[J], P E &R S 2001, 67(10): 1137-1142.
10. Kristil, Sayler. AN ASSESSMENT OF CHANGE VECTOR ANALYSIS USING LANDSAT DATA OVER SIOUX FALLS[D], SOUTH DAKOTA Master of Science South Dakota State University 1998.
11. Ross S, Lunetta Christopher D, Elvidge. Remote Sensing Change Detection Environmental Monitoring Methods and Applications[J], UK 1999.
12. J.F. MAS. Monitoring Land-Cover Changes: A Comparison Of Change Detection Techniques[J], REMOTE SENSING, 1999,20(1): 139-152.
13. Mahesh Paland paul M.Mather. Decision Tree Based Classification of Remotely Sensed Data, the 22nd Asian Conference on Remote Sensing[J], 5-9 November 2001 ,Singapore.
14. John B. Collins and Curtis E. Woodcock (1994). Change-detection Using the G-S Transformation Applied to Forest Mapping Mortality[J], Remote Sensing of Environment, and 1994,50:267-269.
15. Daniel J Hayes and Steven A.Sader. Comparison of Change-Detection Techniques for Monitoring Tropical Forest Cleating and Vegetation Regrowth in a Time Series[J], PE&RS 67(9): 1067-1075.
16. J.L. Seftor, D. Larch. The Use of the Genetic Algorithm to Optimize Rule-based Classifiers for Land Cover Categorization Canadian[J], Journal of Remote Sensing 1995,21(4): 412-420.
17. J.-F. MAS. Monitoring Land-cover Changes: A Comparison of Change Detection Techniques International[J], Journal of Remote Sensing 1999,20(1):139-152.
18. San Miguel-Ayanz, Jes?s and G.S. Biging. Comparison of Single-Stage and Multi-stage Classification approaches for Cover Type Mapping with TM and SPOT Data[J],Remote Sensing of Enviroment 1997,59(1):92-104.
19. John G. Lyon, Ding Yuan, Ross S. Lunetta, and Chris D. Elvidge. A Change Detection Experiment using Vegetation Indices[J], PE&RS 1998, 64(2): 143-150.
20. M.A.Friedl and C.E.Brodley. Decision Tree Classification of Land Cover from Remotely Sensed Data[J], Remote Sens.Environ. 1997,61:399-409.
21. Buheaosier(1999).Classification of Saline Soil Based on Knowledge Discovery and Rule Base System Using Remote Sensing Data[J], Journal of Image and Graphics,4(a)(11):965-969.
22. M.A. Friedl and C.E. Brodley(1997). Decision Tree Classification of Land Cover from Remotely Sensed Data[J], Remote Sensing Environment, 61(3): 399-409.
23. J. -F.MAS. Monitoring Land-cover Changes: A Comparison of Change Detection Techniques[J], International Journal of Remote Sensing, 1999,20(1):139-152.
24. Smits PC, Dellepiane SG, Schowengerdt RA. Quality Assessment of Image Classification Algorithms for Land-cover Mapping: a Reviewand a Proposal for a Cost-based Approach[J], International Journal of Remote Sensing, 1999,20(8): 1461-1486.
25. E.T. Sohl, and S.M. Howard. Regional Chatacterizatin of Land Cover using Multiple
Sources of Data[J], PE&RS, 1998,64(1): 45-57.
26. Lobo, A. Image Segmentation and Discriminat Analysis for the Identification of Land Cover Units in Ecology[J], IEEE Transactions on Geoscience and Remote Sensing, 1997,35(5): 1136-1145.
27. Kontoes, C.C., Rokos, D. The Integration of Spatial Context Information in an Experimental Knowledge based System and the Supervised Relaxation Algorithm two Successful Approaches to Improving SPOT XS Classification[J], International Joumal of Remote Sensing, 1996, 17(16): 3093-3106.
28.iX. Yeh AGO. Principal Component Analysis of Stacked Multi-temporal Images for the Monitoring of Rapid Urban Expansion in the Pearl River Delta[J], International Journal of Remote Sensing, 1998,19(8): 1501-1518.
29.ArtikeyAn B., MAjumder KL, SArkAr A. A Segmentation Approach to Classification of Remote Sensing Imagery[J], International Journal of Remote Sensing, 1998, 19(9): 1695-1709.
30.Hansen, M. Dubayah, R. and DeFries. R. Classification Trees: An Alternative to Traditional Land Cover Classifiers[J], International Journal of Remote Sensing, 1996, 17(5): 1075 - 1082.
31. P.Neville, RI.Coward, RP.Watson, M.Inglis, S.Morain. The Application of TM Imagery and GIS Data in the Assessment of Add Lands Water and Land Resource in West Texas[J], PE&RS2000, 66(11): 1373-1379.
32. Wotawa and G. Wotawa. Improving the Landcover Classification Using Domain Knowledge[J], Intemational Archives of Photogrammetry and Remotesensing, 2000,33(134):8-545.
33. Martinez-Casasnovas JA. A Cartographic and Database Approach for Land Cover/Use Mapping and Generalization from Remotely Sensed data[J], International Joumalof Remote Sensing,2000, 21(9): 1825-1842.
34.Eiumnoh, A. and RP Shrestha. Application of DEM Data to Landsat Image Classification: Evaluation in a Tropical Wet dry Landscape of Thailand[J], PE&RS 2000, 66(3): 297-304.
35. Lawrence, RL, and A. Wright. Rule-based classification systems using classification and regression tree analysis[J], PE&RS2001, 10:1137-1142.
36. Zhenkui Ma, Melissa M. Hart, and Roland L. Redmond. Mapping vegetation across large
geographic areas: Integration of Remote sensing and GIS to classify multisourse data[J], PE&RS 2001, 67(3): 295-307.
37. Deal, Brian Schunk Ⅱ, Daniel. Spatial Dynarnic Modeling for urban Development[J], PE&RS 2001, 67(9):1049-1057.
38. Daniel J. Hayes, Steven A. Sader. Comparison of change-Detection Techniques for monitoring Tropical Forest clearing and vegetation Regrowth in a Time series[J], PE&RS, 2001, 67(9): 1067-1074.
39. Heo, J., and Fitzhugh, TW. A standardized Rad ometric Normalization Method for change Detection Using Remotely sensed Imagery[J], PE&RS2000, 66(2):173-181.
40.Wang ping, Zhang jixian, Lin zongjian. Methods of change extraction in land use/land cover[C], Image Processing And Pattern Recognition in Remote Sensing, SPIE Volume 4898, 2002.10.
41. Wang ping, Zhangjixian, Pang lei, Zheng yonggtto. The Intelligent Decision Analysis Technology of Extracting LUCC Information[C], 7th South East Asian Survey Congress, 2003.11.
42.Wang Ping, Zheng Yongguo, Lin ZongJian, Zhang Jixian, Zhou Chunyan. Extraction of LUCC with the methods differencing and threshold[C],中国有色金属学报(英文版),2004.5.
43.Xiaolong Dai and Siamak Khorram. Remotely Sensed Change Detection based on Artificial Neural Networks[J], PE&RS 1999,65 (10):1187-1994.
44.Alain Royer, Lise Charbonneau And Richard Brochu. Radiometric Comparison Of The Landsat-5 TM And MSS Sensors[J], REMOTE SENSINCG 1987,8(4): 579-591.
45. Unser M. Sum and difference histogram s for texture classification[J], IEEE Trans PAM I-8, Jan 1986.
46.Devijve P A. Pattern Recognition: A statistical approach[M], Englewood Cliffs, NJ: Prentice-Hall, 1982.
47. Alain royer Radiometric comparison of the LANDSAT-5 TM and MSS sensors INT.J. Remote semsing, 1987, 8(4):579-591.
48.Eric F.Lambin and Daniele Ehrlich. Land-cover Changes in Sub-saharan Africa (1982-1991): Application of a change Index Based on Remotely Sensed Surface Temperature and Vegetation Indices at a Continental Scale[J], R emote Sens.Environ, 1997,61:181-200.
49.Lesley-Ann Duplgny-Giroux and John E.Lewis. A Moisture Index for Surface Characterization over a Semiarid Area, PE &RS1999, 65(8): 937-945.
50.Victorino A.Bato, Marvilyn Palaganas. The use of a knowledge-based decision rule computer program in the internal land cover and land use change analysis of the upper magat[C], the 22nd Asian Conference on Remote Sensing, 5-9November, 2001, Singapore, 47-98.
51．王萍，郑永果，张继贤，张运生．基于RS的土地利用／土地覆盖变化信息提取方法—以甘肃石羊河流域为例[J]，资源开发与市场，2003．12．
52．张继贤，程烨。3S技术支持的土地利用现状图更新[J]，中国土地科，2002，2：16(1)：20—25．
53．术洪磊，毛赞猷．GIS辅助下的基于知识的遥感影象分类方法研究[J]，测绘学报，1997，26：328-336．
54．陈晋，何春阳等．基于变化向量分析(CVA)的土地利用／覆盖变化动态监测——变化类型的确定方法[J]，遥感学报，2001，5(5)：346—352．
55．常庆瑞，魏永胜等．土地资源动态遥感监测方法研究[J]，西北农业大学学报，1994，22(4)：118—121．
56．闫守邕．现代遥感技术系统及其发展趋势[J]，遥感学报，1995，10(1)：52—62．
57．童庆禧．遥感科学技术进展[J]，地理学报，1994，第49卷增刊：616—624．
58．赵庚星．遥感土地利用设想与动态监测研究综述[J]，山东农业大学学报，1997，28(1)：67—72．
59．王之卓．遥感与地球的全球性观测[J]，环境遥感，1994，9(3)：161—167．
60．舒宁．一种遥感图象理解专家系统的设计[J]，武汉科技，1995，(1)：12—14．
61．吴炳方，黄绚等．应用遥感及地理信息系统进行植被制图[J]，环境遥感，1995．10(1)：30—37．
62．杨存建，周成虎．基于知识的遥感图像分类方法的探讨[J]，地理学与国土研究，2001，17(1)：72—77．
63．闫守邕，全刚等．在GIS支持下的遥感影像分类、判读与制图系统[J]，应用技术，1995，10(1)：7—14．
64．李四海．提高遥感分类数据分类应用性的有效途径[J]，国土资源遥感，1995，26(4)：2—13．
65．杨存建，周成虎．基于知识发现的TM图像居民自动提取研究[J]，遥感技术与应
用，2001，16(1)：1—6．
66．骆剑承，周成虎等．具有部分监督的遥感影像模糊聚类方法研究及其应用[J]，遥感技术与应用，1999，14(4)：37—43．
67．许祥向．伶仃洋遥感动态监测[J]，国土资源遥感，1994，21(3)：18—24．
68．李秀彬．全球环境变化研究的核心领域——土地利用／土地覆盖变化的国际研究动向[J]，地理学报，1996，51(6)：553—558．
69．骆剑承，周成虎等．人工神经网络遥感影像分类模型及其与知识集成方法研究[J]，遥感学报，2001，15(2)：122-129．
70．江卫，李曦滨．遥感Landsat TM数据在水文地质测绘中的应用[J]，河北建筑科技学院学报，2001，18(1)：69-72．
71．杨存建，刘纪远等．遥感和GIS支持下的云南省退耕还林还草决策分析[J]，地理学报，2001，56(2)．
72．黄宁，刘小军等．遥感图像分类技术研究[J]，华北工学院测试技术学报，2001，(15)：86-92．
73．骆剑承，周成虎．遥感影像生理认知概念模型和方法体系[J]，遥感技术与应用2001，36(2)：103—109．
74．徐冠华．走向二十一世纪的中国地球科学[M]，河南科学出版社，1995．
75．苏理宏，黄裕霞．基于知识的空间决策支持模型集成[J]，遥感学报，2000，4(2)：151-156．
76．杨存建，周成虎．基于知识的遥感图像分类方法的探讨[J]，地理学与国土研究，2001，17(1)：72-77．
77．柳海鹰，高吉喜，李政海．土地覆盖及土地利用遥感研究进展[J]，国土资源遥感，2001，50(4)：7—12．
78．李军，林宗坚．基于特征的遥感影像数据融合方法[J]，中国图象图形学报1997，22(3)：103-107。
79．贾永红．基于像元的遥感影像融合方法的比较[J]．测绘信息与工程，1997，4：29—31．
80．洪家荣等．一种新的决策树归纳学习算法[J]，计算机学报，1995，18(6)：470-473．
81．张继贤，论土地利用与覆盖变化遥感信息提取技术框架[J]，测绘科学，2003，28(3)：13-16．
82．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)：51-53．
83．李爽，张二勋。基于决策树的遥感影像分类方法研究[J]，地域研究与开发，2003，22(2)：17-21．
84．宫鹏，史培军，浦瑞良，郭华东．对地观测技术与地球系统科学[M]，科学出版社，1996．
85．蔡运龙．土地利用／土地覆被变化研究寻求新的综合途径[J]，地理研究，2001，20(6)：645-652．
86．王万森．人工智能原理及其应用[M]，电子工业出版社，北京，2000年9月．
87．付炜．土壤遥感分类识别推理决策器的设计[J]，遥感学报，2001，5(6)：434-441．
88．张凤荣．中国土地资源及其可持续利用[M]，中国农业大学出版社，2000．
89．于兴修，杨桂山．中国土地利用覆被变化研究的现状与问题[J]，地理科学进展，2002，21(1)：51-57．
90．李四海，恽才兴．土地覆盖遥感专题信息的分层提取方法及其应用[J]，遥感技术与应用，1999，14(4)：23-28。
91．李晓兵，国际土地利用-土地覆被变化的环境影响研究[J]，地球科学进展，1999，14(4)：395-400．
92．王萍，张继贤，林宗坚．基于多源遥感数据融合的LUCC信息提取试验[J]，测绘通报．2003．4．
93．韩涛．用TM资料对祁连山部分地区进行针叶林、灌木林分类研究[J]，遥感技术与应用，2002，17(6)：317-321．
94．林济铿，余贻鑫．基于混合决策树-人工神经网络的电力系统动态安全评价[J]，中国电机工程学报，1996，16(6)：378-383．
95．谢雨平，孙山泽．多阶决策的有效工具决策树-应用决策分析(Ⅲ)[J]，树立统计与管理，1996．15(6)：55-58．
96．李飞雪，李满春，赵书河．基于人工神经网络与决策树相结合模型的遥感图像自动分类研究[J]，遥感信息，2003，3：23-26．
97．邓清，林建平，阮雪榆．多概念学习的决策树MNID算法[J]，上海交通大学学报，2003，34(3)：408-410．
98．洪家荣，丁明峰，李星原，王丽薇．一种新的决策树归纳学习算法[J]，计算机学报，1995，18(6)：470-474．
99．梁华金，申深，陈海雯．基于决策树的选案分析模型设计[J]，应用技术，
2002，6：21-23．
100．侯广坤，张劲峰．基于决策树的神经网络规则抽取方法[J]，中山大学学报，2000，39(4)：27-30．
101．黄欣，杨杰，叶晨洲．基于复合式衡量准则的决策树生成算法[J]，上海交通大学学报，2000，34(12)：1687-1690．
102．朱绍文等．决策树采掘技术及发展趋势[J]，计算机工程，2000，26(10)：1-3．
2. William E R, Willam BM, Turner B L. Modeling land use and land cover as part of global environment change[J], Climatic change, 1994, 28(3): 45-64.
3. John G. Lyon, Ding Yuan, Lunetta, and Chris D. Elvidge. A Change Detection Experiment Using Vegetation Indices[J], PE&RS, 1998:143-150.
4. Tucker C J, Townshend J R G, Goff T E. Africa land cover classification using satellite data[J], Science, 1985, 22(7): 369-375.
5. M.J. Hansen, S. E. Franklin, C.Woudsma M.Peterson. Forest Structure Classification in the North Columbia Mountains Using the Landsat TM Tasseled Cap Wetness Component[J], Canadian Journal of Remote Sensing, 2001, 27(1): 20-31
6. M.H. Tangestani F.Moore. Comparison of Three Principal Component Analysis Techniques to Porphyry Copper Alteration Mapping A Case Study, MEIDUK Area, Meiduk Area, Kerman[J], Iran Canadian Journal of Remote Sensing, 2001, 27(2): 176-181.
7. Peter Scarth, Stuart R. Phinn. Integrating High and Moderate Spatial Resolution Image Data to Estimate Forest Age Structure[J], Canadian Journal of Remote Sensing, 2001, 27(2): 129-141.
8. Lesley-Ann Dupign-Giroux and John E. Lewis Moisture. Index for Surface Characterization over a Semiarid Area[J], PE&RS 1999, 65(8): 937-945.
9. Rick L. Lawrence and Andrea Wright. Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis[J], P E &R S 2001, 67(10): 1137-1142.
10. Kristil, Sayler. AN ASSESSMENT OF CHANGE VECTOR ANALYSIS USING LANDSAT DATA OVER SIOUX FALLS[D], SOUTH DAKOTA Master of Science South Dakota State University 1998.
11. Ross S, Lunetta Christopher D, Elvidge. Remote Sensing Change Detection Environmental Monitoring Methods and Applications[J], UK 1999.
12. J.F. MAS. Monitoring Land-Cover Changes: A Comparison Of Change Detection Techniques[J], REMOTE SENSING, 1999,20(1): 139-152.
13. Mahesh Paland paul M.Mather. Decision Tree Based Classification of Remotely Sensed Data, the 22nd Asian Conference on Remote Sensing[J], 5-9 November 2001 ,Singapore.
14. John B. Collins and Curtis E. Woodcock (1994). Change-detection Using the G-S Transformation Applied to Forest Mapping Mortality[J], Remote Sensing of Environment, and 1994,50:267-269.
15. Daniel J Hayes and Steven A.Sader. Comparison of Change-Detection Techniques for Monitoring Tropical Forest Cleating and Vegetation Regrowth in a Time Series[J], PE&RS 67(9): 1067-1075.
16. J.L. Seftor, D. Larch. The Use of the Genetic Algorithm to Optimize Rule-based Classifiers for Land Cover Categorization Canadian[J], Journal of Remote Sensing 1995,21(4): 412-420.
17. J.-F. MAS. Monitoring Land-cover Changes: A Comparison of Change Detection Techniques International[J], Journal of Remote Sensing 1999,20(1):139-152.
18. San Miguel-Ayanz, Jes?s and G.S. Biging. Comparison of Single-Stage and Multi-stage Classification approaches for Cover Type Mapping with TM and SPOT Data[J],Remote Sensing of Enviroment 1997,59(1):92-104.
19. John G. Lyon, Ding Yuan, Ross S. Lunetta, and Chris D. Elvidge. A Change Detection Experiment using Vegetation Indices[J], PE&RS 1998, 64(2): 143-150.
20. M.A.Friedl and C.E.Brodley. Decision Tree Classification of Land Cover from Remotely Sensed Data[J], Remote Sens.Environ. 1997,61:399-409.
21. Buheaosier(1999).Classification of Saline Soil Based on Knowledge Discovery and Rule Base System Using Remote Sensing Data[J], Journal of Image and Graphics,4(a)(11):965-969.
22. M.A. Friedl and C.E. Brodley(1997). Decision Tree Classification of Land Cover from Remotely Sensed Data[J], Remote Sensing Environment, 61(3): 399-409.
23. J. -F.MAS. Monitoring Land-cover Changes: A Comparison of Change Detection Techniques[J], International Journal of Remote Sensing, 1999,20(1):139-152.
24. Smits PC, Dellepiane SG, Schowengerdt RA. Quality Assessment of Image Classification Algorithms for Land-cover Mapping: a Reviewand a Proposal for a Cost-based Approach[J], International Journal of Remote Sensing, 1999,20(8): 1461-1486.
25. E.T. Sohl, and S.M. Howard. Regional Chatacterizatin of Land Cover using Multiple
Sources of Data[J], PE&RS, 1998,64(1): 45-57.
26. Lobo, A. Image Segmentation and Discriminat Analysis for the Identification of Land Cover Units in Ecology[J], IEEE Transactions on Geoscience and Remote Sensing, 1997,35(5): 1136-1145.
27. Kontoes, C.C., Rokos, D. The Integration of Spatial Context Information in an Experimental Knowledge based System and the Supervised Relaxation Algorithm two Successful Approaches to Improving SPOT XS Classification[J], International Joumal of Remote Sensing, 1996, 17(16): 3093-3106.
28.iX. Yeh AGO. Principal Component Analysis of Stacked Multi-temporal Images for the Monitoring of Rapid Urban Expansion in the Pearl River Delta[J], International Journal of Remote Sensing, 1998,19(8): 1501-1518.
29.ArtikeyAn B., MAjumder KL, SArkAr A. A Segmentation Approach to Classification of Remote Sensing Imagery[J], International Journal of Remote Sensing, 1998, 19(9): 1695-1709.
30.Hansen, M. Dubayah, R. and DeFries. R. Classification Trees: An Alternative to Traditional Land Cover Classifiers[J], International Journal of Remote Sensing, 1996, 17(5): 1075 - 1082.
31. P.Neville, RI.Coward, RP.Watson, M.Inglis, S.Morain. The Application of TM Imagery and GIS Data in the Assessment of Add Lands Water and Land Resource in West Texas[J], PE&RS2000, 66(11): 1373-1379.
32. Wotawa and G. Wotawa. Improving the Landcover Classification Using Domain Knowledge[J], Intemational Archives of Photogrammetry and Remotesensing, 2000,33(134):8-545.
33. Martinez-Casasnovas JA. A Cartographic and Database Approach for Land Cover/Use Mapping and Generalization from Remotely Sensed data[J], International Joumalof Remote Sensing,2000, 21(9): 1825-1842.
34.Eiumnoh, A. and RP Shrestha. Application of DEM Data to Landsat Image Classification: Evaluation in a Tropical Wet dry Landscape of Thailand[J], PE&RS 2000, 66(3): 297-304.
35. Lawrence, RL, and A. Wright. Rule-based classification systems using classification and regression tree analysis[J], PE&RS2001, 10:1137-1142.
36. Zhenkui Ma, Melissa M. Hart, and Roland L. Redmond. Mapping vegetation across large
geographic areas: Integration of Remote sensing and GIS to classify multisourse data[J], PE&RS 2001, 67(3): 295-307.
37. Deal, Brian Schunk Ⅱ, Daniel. Spatial Dynarnic Modeling for urban Development[J], PE&RS 2001, 67(9):1049-1057.
38. Daniel J. Hayes, Steven A. Sader. Comparison of change-Detection Techniques for monitoring Tropical Forest clearing and vegetation Regrowth in a Time series[J], PE&RS, 2001, 67(9): 1067-1074.
39. Heo, J., and Fitzhugh, TW. A standardized Rad ometric Normalization Method for change Detection Using Remotely sensed Imagery[J], PE&RS2000, 66(2):173-181.
40.Wang ping, Zhang jixian, Lin zongjian. Methods of change extraction in land use/land cover[C], Image Processing And Pattern Recognition in Remote Sensing, SPIE Volume 4898, 2002.10.
41. Wang ping, Zhangjixian, Pang lei, Zheng yonggtto. The Intelligent Decision Analysis Technology of Extracting LUCC Information[C], 7th South East Asian Survey Congress, 2003.11.
42.Wang Ping, Zheng Yongguo, Lin ZongJian, Zhang Jixian, Zhou Chunyan. Extraction of LUCC with the methods differencing and threshold[C],中国有色金属学报(英文版),2004.5.
43.Xiaolong Dai and Siamak Khorram. Remotely Sensed Change Detection based on Artificial Neural Networks[J], PE&RS 1999,65 (10):1187-1994.
44.Alain Royer, Lise Charbonneau And Richard Brochu. Radiometric Comparison Of The Landsat-5 TM And MSS Sensors[J], REMOTE SENSINCG 1987,8(4): 579-591.
45. Unser M. Sum and difference histogram s for texture classification[J], IEEE Trans PAM I-8, Jan 1986.
46.Devijve P A. Pattern Recognition: A statistical approach[M], Englewood Cliffs, NJ: Prentice-Hall, 1982.
47. Alain royer Radiometric comparison of the LANDSAT-5 TM and MSS sensors INT.J. Remote semsing, 1987, 8(4):579-591.
48.Eric F.Lambin and Daniele Ehrlich. Land-cover Changes in Sub-saharan Africa (1982-1991): Application of a change Index Based on Remotely Sensed Surface Temperature and Vegetation Indices at a Continental Scale[J], R emote Sens.Environ, 1997,61:181-200.
49.Lesley-Ann Duplgny-Giroux and John E.Lewis. A Moisture Index for Surface Characterization over a Semiarid Area, PE &RS1999, 65(8): 937-945.
50.Victorino A.Bato, Marvilyn Palaganas. The use of a knowledge-based decision rule computer program in the internal land cover and land use change analysis of the upper magat[C], the 22nd Asian Conference on Remote Sensing, 5-9November, 2001, Singapore, 47-98.
51．王萍，郑永果，张继贤，张运生．基于RS的土地利用／土地覆盖变化信息提取方法—以甘肃石羊河流域为例[J]，资源开发与市场，2003．12．
52．张继贤，程烨。3S技术支持的土地利用现状图更新[J]，中国土地科，2002，2：16(1)：20—25．
53．术洪磊，毛赞猷．GIS辅助下的基于知识的遥感影象分类方法研究[J]，测绘学报，1997，26：328-336．
54．陈晋，何春阳等．基于变化向量分析(CVA)的土地利用／覆盖变化动态监测——变化类型的确定方法[J]，遥感学报，2001，5(5)：346—352．
55．常庆瑞，魏永胜等．土地资源动态遥感监测方法研究[J]，西北农业大学学报，1994，22(4)：118—121．
56．闫守邕．现代遥感技术系统及其发展趋势[J]，遥感学报，1995，10(1)：52—62．
57．童庆禧．遥感科学技术进展[J]，地理学报，1994，第49卷增刊：616—624．
58．赵庚星．遥感土地利用设想与动态监测研究综述[J]，山东农业大学学报，1997，28(1)：67—72．
59．王之卓．遥感与地球的全球性观测[J]，环境遥感，1994，9(3)：161—167．
60．舒宁．一种遥感图象理解专家系统的设计[J]，武汉科技，1995，(1)：12—14．
61．吴炳方，黄绚等．应用遥感及地理信息系统进行植被制图[J]，环境遥感，1995．10(1)：30—37．
62．杨存建，周成虎．基于知识的遥感图像分类方法的探讨[J]，地理学与国土研究，2001，17(1)：72—77．
63．闫守邕，全刚等．在GIS支持下的遥感影像分类、判读与制图系统[J]，应用技术，1995，10(1)：7—14．
64．李四海．提高遥感分类数据分类应用性的有效途径[J]，国土资源遥感，1995，26(4)：2—13．
65．杨存建，周成虎．基于知识发现的TM图像居民自动提取研究[J]，遥感技术与应
用，2001，16(1)：1—6．
66．骆剑承，周成虎等．具有部分监督的遥感影像模糊聚类方法研究及其应用[J]，遥感技术与应用，1999，14(4)：37—43．
67．许祥向．伶仃洋遥感动态监测[J]，国土资源遥感，1994，21(3)：18—24．
68．李秀彬．全球环境变化研究的核心领域——土地利用／土地覆盖变化的国际研究动向[J]，地理学报，1996，51(6)：553—558．
69．骆剑承，周成虎等．人工神经网络遥感影像分类模型及其与知识集成方法研究[J]，遥感学报，2001，15(2)：122-129．
70．江卫，李曦滨．遥感Landsat TM数据在水文地质测绘中的应用[J]，河北建筑科技学院学报，2001，18(1)：69-72．
71．杨存建，刘纪远等．遥感和GIS支持下的云南省退耕还林还草决策分析[J]，地理学报，2001，56(2)．
72．黄宁，刘小军等．遥感图像分类技术研究[J]，华北工学院测试技术学报，2001，(15)：86-92．
73．骆剑承，周成虎．遥感影像生理认知概念模型和方法体系[J]，遥感技术与应用2001，36(2)：103—109．
74．徐冠华．走向二十一世纪的中国地球科学[M]，河南科学出版社，1995．
75．苏理宏，黄裕霞．基于知识的空间决策支持模型集成[J]，遥感学报，2000，4(2)：151-156．
76．杨存建，周成虎．基于知识的遥感图像分类方法的探讨[J]，地理学与国土研究，2001，17(1)：72-77．
77．柳海鹰，高吉喜，李政海．土地覆盖及土地利用遥感研究进展[J]，国土资源遥感，2001，50(4)：7—12．
78．李军，林宗坚．基于特征的遥感影像数据融合方法[J]，中国图象图形学报1997，22(3)：103-107。
79．贾永红．基于像元的遥感影像融合方法的比较[J]．测绘信息与工程，1997，4：29—31．
80．洪家荣等．一种新的决策树归纳学习算法[J]，计算机学报，1995，18(6)：470-473．
81．张继贤，论土地利用与覆盖变化遥感信息提取技术框架[J]，测绘科学，2003，28(3)：13-16．
82．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)：51-53．
83．李爽，张二勋。基于决策树的遥感影像分类方法研究[J]，地域研究与开发，2003，22(2)：17-21．
84．宫鹏，史培军，浦瑞良，郭华东．对地观测技术与地球系统科学[M]，科学出版社，1996．
85．蔡运龙．土地利用／土地覆被变化研究寻求新的综合途径[J]，地理研究，2001，20(6)：645-652．
86．王万森．人工智能原理及其应用[M]，电子工业出版社，北京，2000年9月．
87．付炜．土壤遥感分类识别推理决策器的设计[J]，遥感学报，2001，5(6)：434-441．
88．张凤荣．中国土地资源及其可持续利用[M]，中国农业大学出版社，2000．
89．于兴修，杨桂山．中国土地利用覆被变化研究的现状与问题[J]，地理科学进展，2002，21(1)：51-57．
90．李四海，恽才兴．土地覆盖遥感专题信息的分层提取方法及其应用[J]，遥感技术与应用，1999，14(4)：23-28。
91．李晓兵，国际土地利用-土地覆被变化的环境影响研究[J]，地球科学进展，1999，14(4)：395-400．
92．王萍，张继贤，林宗坚．基于多源遥感数据融合的LUCC信息提取试验[J]，测绘通报．2003．4．
93．韩涛．用TM资料对祁连山部分地区进行针叶林、灌木林分类研究[J]，遥感技术与应用，2002，17(6)：317-321．
94．林济铿，余贻鑫．基于混合决策树-人工神经网络的电力系统动态安全评价[J]，中国电机工程学报，1996，16(6)：378-383．
95．谢雨平，孙山泽．多阶决策的有效工具决策树-应用决策分析(Ⅲ)[J]，树立统计与管理，1996．15(6)：55-58．
96．李飞雪，李满春，赵书河．基于人工神经网络与决策树相结合模型的遥感图像自动分类研究[J]，遥感信息，2003，3：23-26．
97．邓清，林建平，阮雪榆．多概念学习的决策树MNID算法[J]，上海交通大学学报，2003，34(3)：408-410．
98．洪家荣，丁明峰，李星原，王丽薇．一种新的决策树归纳学习算法[J]，计算机学报，1995，18(6)：470-474．
99．梁华金，申深，陈海雯．基于决策树的选案分析模型设计[J]，应用技术，
2002，6：21-23．
100．侯广坤，张劲峰．基于决策树的神经网络规则抽取方法[J]，中山大学学报，2000，39(4)：27-30．
101．黄欣，杨杰，叶晨洲．基于复合式衡量准则的决策树生成算法[J]，上海交通大学学报，2000，34(12)：1687-1690．
102．朱绍文等．决策树采掘技术及发展趋势[J]，计算机工程，2000，26(10)：1-3．