多尺度林业遥感数据融合技术的应用研究
摘要
由于地理现象的等级层次性和地表的复杂性,带来了遥感研究的空间尺度问题,尺度理论作为地理信息科学的基础理论,也是地理数据的基本性质之一。目前高空间分辨率传感器的发展趋势,造成了遥感信息海量化的现状。林业遥感作为遥感的分支之一,同样有着等级层次的特征,如何融合利用这些冗余的多尺度数据资源,从而提高林业遥感应用中的精度,已经成为全世界林业科技工作者急需解决的问题之一。
遥感图像反映的是地学环境的综合性与复杂性特征,而数学方法则是最有力的分析工具。因此,本论文采用数学的方法,根据不同遥感数据源空间分辨率不同的特点,以尺度相关理论为基础,利用多源信息融合理论,挖掘多尺度林业遥感数据的冗余性及互补性,研究多尺度林业遥感数据融合技术的应用问题。
基于多源信息融合的优势和制约因素,围绕着尺度的关键问题,本研究以森林资源信息为对象,以像素→特征→决策三个不同的融合级别为主线,研究的主要内容如下：
1.在数据预处理阶段,根据不同的数据特点,采用了不同的几何校正模型,比较选择了合理的地形辐射校正方法,从而解决了融合中信息类型的高度相异性和内容的模糊性难题。
2.通过一系列的方法,来降低多尺度数据融合带来的复杂性,这些方法包括了波段组合选择、基于景观生态学的廊道思路对研究区域进行区域分割等。
3.通过多个评价指标对常用的像素级融合方法进行比较分析,选择了适合的融合方法,据此实现了六种不同空间尺度的多光谱数据源,扩展了光谱空间的覆盖范围。
4.针对面向对象分割的评价准则,通过构造平均分割评价指数,得到最优分割尺度参数,从而获得最佳的分割对象集,这不仅丰富了分割评价方法的研究,也将面向像素的传统分析方法转变为面向对象的研究。
5.根据尺度研究的关键问题,选择了其中的三个主要内容：最优尺度选择、尺度效应、尺度依赖进行了研究。通过对传统的局部变异和变异函数进行改进,从空间角度为遥感影像的选择提供了客观依据；利用分形几何理论中的分形维数,对遥感的尺度效应提供了研究；基于不同尺度间的对象继承关系,以灰度线性可加性为条件,对不同数据源之间的依赖关系进行了量化研究,并得到了不同尺度针对不同景观区域类型的依赖关系,也使得不同尺度间拓扑关系的研究建立在科学的量化基础上。
6.基于尺度依赖的研究成果,通过构造多尺度的对象约束融合模型,在不改变尺度应用抽象程度的基础上,利用知识分类的结果,实现了不同尺度之间的融合,而实验结果也证明了多尺度融合对林业遥感的贡献程度,这也是本研究的主要创新点。
Because of the hierarchy of geographical phenomena and complicated landcover types, the issue of spatial scale has been brought to the remote sensing research. As the basis of geographic information science theory, scale theory is also one of the basic properties of geographic data. Now the development of high spatial resolution sensors has brought the status of remote sensing information's sea quantify. Forestry remote sensing is the branch of remote sensing, and hierarchy is also its character. How to use these redundant data resources and increase the accuracy in forestry remote sensing, which has become one of the issues need to be resolved to forest scientists around the world.
Remote sensing images reflect the combining and complicacy of environment in geoscience, in addition mathematical method is the powerful analysis tool which is the main method in the research. According to the different spatial resolution of remote sensing data, as the scale relevantly theory basis, using multi-source information fusion theory, mining the redundancy and complementarity of multi-scale remote sensing data, research the applications of multi-scale remote sensing data fusion technology in forest.
Considering the advantages and constraints of multi-source information fusion, concerning the key issues in scale study, as the forest resource information research object, the research line consists of three rank, they are pixel, feture and decision. The paper's main contents as following:
1 In data pre-processing step, different geometric correction models are used for different data types. By comparing results produced by different methods, selecting a reasonable method for radiometric correction of terrain, and this solves the high dissimilarity of information and reduce the ambiguity in contents.
2 Through a series of methods to reduce the complexity of study, as selecting different bands combination, based on the corridor ideas of landscape ecology, the study area is partitioned to different regions.
3 Through comparing the common resolution fusion methods by multiple evaluation indexes, the adaptive fusion method is obtained. This achieve multi-spectral data sources in six different spatial scales, the coverage of the spectral space is expanded.
4 According to the evaluation criteria in object-oriented segmentation, by constructing average segmentation evaluation index, the optimal segmentation scale parameters and the best segmentation object sets are obtained, which not only enriches the segmentation evaluation method, but also change the traditional method for the pixels into object-oriented research.
5 According scale key issues research, three main fields are selected in this study such as optimal scale selection, scale effects and scale dependence. Through improving the limitations to adapt to multi-spectral research of local variance and variograms, an objective basis to the choice of remote sensing images from space is provided. By fractal geometry theory and fractal dimension, the remote sensing scaling effect is researched. Based on the inheritance of objects with different scale levels, as the additive of gray linear a condition, a quantitative study about the dependency of different data sources is given, and the dependency relation results are obtained of different scales for different types in diverse landscape areas, while makes the topological relations research during different scales based on a scientific quantitative basis.
6 Based on the research results of scale dependence, by constructing multi-scale objects constraints fusion model, without changing the basis of scale abstraction level, with the results of classification on basis of knowledge, the fusion of different scale datas come to true. By the results of experiment, the contribution of multi-scale fusion method to forestry remote sensing is validated, which is the innovation point of the research.
遥感图像反映的是地学环境的综合性与复杂性特征,而数学方法则是最有力的分析工具。因此,本论文采用数学的方法,根据不同遥感数据源空间分辨率不同的特点,以尺度相关理论为基础,利用多源信息融合理论,挖掘多尺度林业遥感数据的冗余性及互补性,研究多尺度林业遥感数据融合技术的应用问题。
基于多源信息融合的优势和制约因素,围绕着尺度的关键问题,本研究以森林资源信息为对象,以像素→特征→决策三个不同的融合级别为主线,研究的主要内容如下：
1.在数据预处理阶段,根据不同的数据特点,采用了不同的几何校正模型,比较选择了合理的地形辐射校正方法,从而解决了融合中信息类型的高度相异性和内容的模糊性难题。
2.通过一系列的方法,来降低多尺度数据融合带来的复杂性,这些方法包括了波段组合选择、基于景观生态学的廊道思路对研究区域进行区域分割等。
3.通过多个评价指标对常用的像素级融合方法进行比较分析,选择了适合的融合方法,据此实现了六种不同空间尺度的多光谱数据源,扩展了光谱空间的覆盖范围。
4.针对面向对象分割的评价准则,通过构造平均分割评价指数,得到最优分割尺度参数,从而获得最佳的分割对象集,这不仅丰富了分割评价方法的研究,也将面向像素的传统分析方法转变为面向对象的研究。
5.根据尺度研究的关键问题,选择了其中的三个主要内容：最优尺度选择、尺度效应、尺度依赖进行了研究。通过对传统的局部变异和变异函数进行改进,从空间角度为遥感影像的选择提供了客观依据；利用分形几何理论中的分形维数,对遥感的尺度效应提供了研究；基于不同尺度间的对象继承关系,以灰度线性可加性为条件,对不同数据源之间的依赖关系进行了量化研究,并得到了不同尺度针对不同景观区域类型的依赖关系,也使得不同尺度间拓扑关系的研究建立在科学的量化基础上。
6.基于尺度依赖的研究成果,通过构造多尺度的对象约束融合模型,在不改变尺度应用抽象程度的基础上,利用知识分类的结果,实现了不同尺度之间的融合,而实验结果也证明了多尺度融合对林业遥感的贡献程度,这也是本研究的主要创新点。
Because of the hierarchy of geographical phenomena and complicated landcover types, the issue of spatial scale has been brought to the remote sensing research. As the basis of geographic information science theory, scale theory is also one of the basic properties of geographic data. Now the development of high spatial resolution sensors has brought the status of remote sensing information's sea quantify. Forestry remote sensing is the branch of remote sensing, and hierarchy is also its character. How to use these redundant data resources and increase the accuracy in forestry remote sensing, which has become one of the issues need to be resolved to forest scientists around the world.
Remote sensing images reflect the combining and complicacy of environment in geoscience, in addition mathematical method is the powerful analysis tool which is the main method in the research. According to the different spatial resolution of remote sensing data, as the scale relevantly theory basis, using multi-source information fusion theory, mining the redundancy and complementarity of multi-scale remote sensing data, research the applications of multi-scale remote sensing data fusion technology in forest.
Considering the advantages and constraints of multi-source information fusion, concerning the key issues in scale study, as the forest resource information research object, the research line consists of three rank, they are pixel, feture and decision. The paper's main contents as following:
1 In data pre-processing step, different geometric correction models are used for different data types. By comparing results produced by different methods, selecting a reasonable method for radiometric correction of terrain, and this solves the high dissimilarity of information and reduce the ambiguity in contents.
2 Through a series of methods to reduce the complexity of study, as selecting different bands combination, based on the corridor ideas of landscape ecology, the study area is partitioned to different regions.
3 Through comparing the common resolution fusion methods by multiple evaluation indexes, the adaptive fusion method is obtained. This achieve multi-spectral data sources in six different spatial scales, the coverage of the spectral space is expanded.
4 According to the evaluation criteria in object-oriented segmentation, by constructing average segmentation evaluation index, the optimal segmentation scale parameters and the best segmentation object sets are obtained, which not only enriches the segmentation evaluation method, but also change the traditional method for the pixels into object-oriented research.
5 According scale key issues research, three main fields are selected in this study such as optimal scale selection, scale effects and scale dependence. Through improving the limitations to adapt to multi-spectral research of local variance and variograms, an objective basis to the choice of remote sensing images from space is provided. By fractal geometry theory and fractal dimension, the remote sensing scaling effect is researched. Based on the inheritance of objects with different scale levels, as the additive of gray linear a condition, a quantitative study about the dependency of different data sources is given, and the dependency relation results are obtained of different scales for different types in diverse landscape areas, while makes the topological relations research during different scales based on a scientific quantitative basis.
6 Based on the research results of scale dependence, by constructing multi-scale objects constraints fusion model, without changing the basis of scale abstraction level, with the results of classification on basis of knowledge, the fusion of different scale datas come to true. By the results of experiment, the contribution of multi-scale fusion method to forestry remote sensing is validated, which is the innovation point of the research.
引文
1.柏延臣,王劲峰.基于统计可分性的遥感数据专题分类尺度效应分析[J].遥感技术与应用,2004,19(6)：443-449.
2.柏延臣,王劲峰.遥感数据专题分类不确定性评价研究：进展、问题与展望[J].地球科学进展,2005,20(11)：1218-1225.
3.陈春雷,武刚.遥感影像中像素级融合方法的评价研究[J].浙江林业科技,2009,29(4)：59-64.
4.陈钢,欧阳明,俞帆,赵逸云,肖楠楠.物质表面图像的信息维研究[J].云南大学学报(自然科学版),2000,22(2)：109-112.
5.陈巧,陈永富.QuickBird遥感数据监测植被覆盖度的研究[J].林业科学研究,2005,18(4)：375-380.
6.陈秋晓,骆剑承,周成虎,郑江,鲁学军,沈占锋.基于多特征的遥感影像分类方法[J].遥感学报,2004,8(3)：239-245.
7.陈述彭,程维明.世界森林的数字地球监测[J].遥感学报,2001,5(5)：321-326.
8.陈述彭,赵英时.遥感地学分析[M].北京：测绘出版社,1990：5-27.
9.陈述彭.地球信息科学[M].北京：高等教育出版社,2007：1-147.
10.陈忠,赵忠明.基于区域生长的多尺度遥感图像分割算法[J].计算机工程与应用,2005,41(35)：7-9.
11.承继成,郭华东,史文中.遥感数据的不确定性问题[M].北京：科学出版社,2004：1-29.
12.戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析[M].北京：清华大学出版社,2004：186-186.
13.杜世宏,王桥,秦其明.空间关系模糊描述与组合推理[M].北京：科学出版社,2007：203-221.
14.段四波,阎广建.山区遥感图像地形校正模型研究综述[J].北京师范大学学报(自然科学版),2007,43(3)：362-366.
15.冯种葵,叶晓端Landsat 7卫星快速格式数据产品[J].遥感技术与应用,2000,15(4)：270-273.
16.付卓,胡吉平,谭衢霖,刘正军.遥感应用分析中影像分割方法[J].遥感技术与应用,2006,21(5)：456-462.
17.高永年,张万昌.遥感影像地形校正研究进展及其比较实验[J].地理研究,2008,27(2)：467-476.
18.关元秀,程晓阳.高分辨率卫星影像处理指南[M].北京：科学出版社,2008：141-221.
19.关泽群,刘继琳.遥感图像解译[M].武汉：武汉大学出版社,2007：1-14.
20.国家遥感中心.地球空间信息科学技术进展[M].北京：电子工业出版社,2009：79-163.
21.郭雷,李晖晖,鲍永生.图像融合[M].北京：电子工业出版社,2008：228-248.
22.郭庆胜,杜晓初,闫卫阳.地理空间推理[M].北京：科学出版社,2006：205-213.
23.韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京：清华大学出版社,2006：1-13.
24.韩鹏,龚建雅,李志林.基于信息熵的遥感分类最优空间尺度选择方法[J].武汉大学学报(信息科学版),2008,33(7)：676-679.
25.韩鹏,龚建雅.遥感尺度选择问题研究进展[J].遥感信息,2008,(1)：96-99.
26.何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学,2009,29(1)：106-109.
27.胡兴树,龚建雅,潘建平.当代遥感技术的现状和发展趋势[J].武汉大学学报(工学版),2003,36(3A)：195-198.
28.黄慧萍,吴炳芳.地物大小、对象尺度、影像分辨率的关系分析[J].遥感技术与应用,2006,21(3)：244-248.
29.黄慧萍,吴炳方.基于区域合并影像分割技术的多尺度地表景观分析[J].地理科学进展,2004,23(3)：9-15.
30.贾海峰,刘雪华.环境遥感原理与应用[M].北京：清华大学出版社,2006：112-141.
31.赖震刚,王继.利用ERDAS IMAGINE进行影像的几何精校正[J].现代测绘,2003,26(2)：38-40.
32.李春华,徐涵秋.高分辨率遥感图像融合的光谱保真问题[J].地球信息科学,2008,10(4)：620-526.
33.李德仁.论21世纪遥感与GIS的发展[J].武汉大学学报(信息科学版),2003,28(2)：127-131.
34.李君,范文义,李明泽.线性混合光谱模型在多光谱遥感影像分类中的应用[J].东北林业大学学报,2008,36(8)：45-48.
35.李俊杰,何隆华,戴锦芳,李金莲.遥感影像像元级融合方法与试验评价分析[J].地球信息科学,2008,10(1)：128-134.
36.李霖,吴凡.空间数据多尺度表达模型及其可视化[M].北京：科学出版社,2005：1-53.
37.李霖,应申.空间尺度基础性问题研究[J].武汉大学学报(信息科学版),2005,30(3)：199-203.
38.李明峰,冯宝红,刘三枝.GPS定位技术及其应用[M].北京：国防工业出版社,2006：18-37.
39.李爽,姚静.基于分形的DEM数据不确定性研究[M].北京：科学出版社,2007：69-89.
40.李爽成,蔡运龙.地理尺度转换若干问题的初步探讨[J].地理研究,2005,24(1)：11-18.
41.李素.TM/ETM+遥感影像混合像元分解及其应用研究[M].北京：中国农业大学出版社,2009：6-16.
42.李小文.定量遥感的发展与创新[J].河南大学学报,2005,35(4)：49-56.
43.李征航,黄劲松.GPS测量与数据处理[M].武昌：武汉大学出版社,2007：167-171.
44.梁顺林著,范闻捷等译.定量遥感[M].北京：科学出版社,2009：169-178.
45.刘凯,秦耀辰.论地理信息的尺度特性[J].地理与地理信息科学,2010,26(2)：1-5.
46.刘凯,任星,秦耀辰.地理信息语义尺度及其变换机制问题研究[J].河南大学学报(自然科学版),2010,40(3)：261-266.
47.刘明德,林杰斌.地理信息系统GIS理论与实务[M].北京：清华大学出版社,2006：182-210.
48.刘仁钊,廖文峰.遥感图像分类应用研究综述[J].地理空间信息,2005,3(5)：11-13.
49.刘贤赵,张安定,李嘉竹.地理学数学方法[M].北京：科学出版社,2009：1-75.
50.刘勇,刘悦翠,王周.SPOT5卫星影像在森林资源规划设计调查中的应用[J].安徽农业科学,2007,35(27)：8552-8553
51.栾庆祖,刘慧平,肖志强.遥感影像的正射校正方法比较[J].遥感技术与应用,2007,22(6)：743-747.
52.罗忠.多源遥感数据融合的现状[J].测试技术学报,1999,13(1)：32-38.
53.吕一河,博伯杰.生态学中的尺度及尺度转换方法[J].生态学报,2001,21(12)：2096-2105.
54.马蔼乃.理论地理科学与哲学：复杂性科学理论[M].北京：高等教育出版社,2007：88-191.
55.马克明,祖元刚.兴安落叶松种群格局的分性特征一信息维数[J].生态学报,2000,20(2)：187-192.
56.苗东升.系统科学大学讲稿[M].北京：中国人民大学出版社,2007：338-354.
57.苗东升.系统科学精要(第2版)[M].北京：中国人民大学出版社,2006：197-204.
58.那彦,焦李成.基于多分辨率分析理论的图像融合方法[M].西安：西安电子科技大学出版社,2007：83-172.
59.年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择—以云岭中部山区为例[J].云南地理环境研究,2004,16(2)：18-21.
60.潘玉君,武友德.地理科学导论[M].北京：科学出版社,2009：73-83.
61.彭望琭等.遥感概论[M].北京：高等教育出版社,2003：1-24.
62.钱乐祥.遥感数字影像处理与地理特征提取[M].北京：科学出版社,2004：183-221.
63.邵景安,陈兰,李阳兵,魏朝富,谢德体.未来区域土地利用驱动力研究的重要命题：尺度依赖[J].资源科学,2008,30(1)：58-63.
64.沈润平,王人潮,赵小敏.最佳特征影像组合选择指数的研究[J].华南农业大学学报(自然科学版),2002,23(2)：18-21.
65.史榕,许惠平,陈华根.遥感影像数据融合技术及融合质量评价研究[J].鲁东大学学报(自然科学版),2008,24(2)：130-134.
66.史文中,王树良.GIS之属性不确定性研究[J].中国图形图像学报,2001,6(9)：918-923.
67.苏理宏,李小文,黄裕霞.遥感尺度问题研究进展[J].地球科学进展,2001,16(4)：544-548.
68.孙家抦.遥感原理与应用[M].武汉：武汉大学出版社,2005：98-141.
69.孙小芳,卢健,孙依斌.城市绿地多尺度分割提取及景观分析[J].遥感信息,2006,(2)：31-33.
70.谭兵,邢帅,徐青,李建胜.SPOT5超模式数据处理技术研究[J].遥感技术与应用,2004,19(4)：249-252.
71.谭炳香.新世纪遥感与林业信息需求[J].遥感信息,2002,(2)：32-37.
72.谭皓,万彦辉.GPS定位的坐标转换[J].导航与控制,2004,3(3)：6-9.
73.谭衢霖,刘正军,沈伟.一种面向对象的遥感影像多尺度分割方法[J].北京交通大学学报,2007,31(4)：111-119.
74.谭永生,沈掌泉,贾春燕,王新辉,邓劲松.中高分辨率遥感影像融合研究[J].遥感技术与应用,2007,22(4)：536-542.
75.王爱民.地理学思想史[M].北京：科学出版社,2010：262-281.
76.王倩,张起鹏.分形理论在遥感图像分类处理中的应用综述[J].河北遥感,2008,(4)：14-15.
77.王桥,毋河海.地图信息的分形描述与自动综合研究[M].武汉：武汉测绘科技大学出版社,1998：1-92.
78.王淑,王恒山,肖刚.多源遥感影像融合理论、技术和应用[J].微型电脑应用,2005,21(12)：1-5.
79.王艳慧,孟浩.GIS中地理要素多尺度表达间层次连通性的研究[J].湖南科技大学学报,2006,12(1)：59-63.
80.王政权.地统计学及在生态学中的应用[M].北京：科学出版社,1999：35-101.
81.汪自军,陈圣波,韩念龙,湛邵斌,吕航.地学尺度转换理论及方法研究[J].地理空间信息,2007,5(4)：60-63.
82.韦玉春,汤国安,杨昕.遥感数字图像处理教程[M].北京：科学出版社,2007：177-255.
83.魏克让,江聪世.空间数据的误差处理[M].北京：科学出版社,2005：9-24.
84.邬建国.景观生态学[M].北京：高等教育出版社,2007：25-158.
85.吴建平.分形理论在遥感中的应用[J].遥感技术与应用,1993,8(4)：23-27.
86.肖奥,陶舒,王晓爽,张彤欣.遥感融合方法分析与评价[J].首都师范大学学报(自然科学版),2007,28(4)：77-80.
87.肖笃宁,李秀珍,高峻,常禹,李团胜.景观生态学[M].北京：科学出版社,2007：17-59.
88.徐建华,岳文泽,谈文琦.城市景观格局尺度效应的空间规律—以上海中心城区为例[J].地理学报,2004,59(6)：1058-1067.
89.徐青,潭国光.遥感数据的分形测量[J].遥感学报,1998,2(3)：186-192.
90.徐青,张艳,耿则勋,刑帅,谭兵.遥感影像融合与分辨率增强技术[M].北京：科学技术出版 社,2007：68-128.
91.徐庆铃,彭世揆.TM影像地形辐射校正的应用研究--以广东九曲水林场为例[D].南京林业大学,2008：23-32.
92.许榕峰,徐涵秋.ETM+全色波段及其多光谱波段图像的融合应用[J].地球信息科学,2004,6(1)：99-103.
93.闫海文,褚衍东.多尺度地图空间相似关系基本问题研究[J].地理与地理信息科学,2009,25(4)：42-44.
94.杨晖,曲秀杰.图像分割方法综述[J].电脑开发与应用,2005,18(3)：21-23.
95.杨金红,尹球,顾松山,周宁,刘晓明.城区高光谱遥感数据假彩色波段组合研究[J].南京气象学院学报,2005,28(3)：289-295.
96.杨燕,田庆久.森林覆盖区山地遥感地形校正的方法研究[J].遥感信息,2008,(1)：22-26.
97.尹占娥.现代遥感导论[M].北京：科学出版社,2008：218-221.
98.游先祥.遥感原理及其在资源环境中的应用[M].北京：中国林业出版社,2003：1-12.
99.袁传武,柯尊胜,陈双田,吴翠,王珠娜,崔鸿侠.多源遥感数据融合原理与模型结构及应用[J].西南林学院学报,2007,27(5)：14-17.
100.詹勤,李德仁,眭海钢,张霞.一种遥感信息服务分类本体构建方法[J].武汉大学学报(信息科学版),2010,35(3)：343-346.
101.张超,王人潮,赵春江,李海霞,刘良云.山区高分辨率遥感影像地形辐射校正方法研究[J].浙江大学学报(农业与生命科学版),2006,32(6)：693-698.
102.张洪亮,倪绍祥,张军.国外遥感图像的地形归一化方法研究进展[J].遥感信息,2001,(3)：24-29.
103.张济忠.分形[M].北京：清华大学出版社,2006：7-79.
104.张景雄.空间信息的尺度、不确定性与融合[M].武汉大学出版社,2008：1-19.
105.张俊,王宝山,吕宝庆.面向对象高分辨率影像信息提取中的尺度效应研究[J].北京测绘,2010,(1)：11-14.
106.张俊,汪云甲,李妍,王行凤.一种面向对象的高分辨率影像最优分割尺度选择算法[J].科技导报,2009,27(21)：91-94.
107.张永生,巩丹超.高分辨率遥感卫星应用—成像模型、处理算法及应用技术[M].北京：科学出版社,2005：15-51.
108.张煜星,王祝雄.遥感技术在森林资源清查中的应用研究[M].北京：中国林业出版社,2007：1-14.
109.章毓晋.图象工程(上册)[M].北京：清华大学出版社,2006：365-387.
110.章毓晋.图象工程(中册)[M].北京：清华大学出版社,2006：73-105.
111.章毓晋.图象工程(下册)[M].北京：清华大学出版社,2007：201-227.
112.赵茂程,高素萍,潘一凡,郑加强.分形理论及其在林业中的应用与研究进展[J].世界林业研究,2002,15(2)：28-34.
113.赵书河.多源遥感影像融合技术与应用[M].南京：南京大学出版社,2008：1-35.
114.赵文武,傅伯杰,陈利顶.尺度推绎中的几点基本问题[J].地球科学进展,2002,17(6)：905-911.
115.赵宪文,李崇贵,斯林,袁凯先,田永林.基于信息技术的森林资源调查新体系[J].北京林业大学学报,2002,24(5/6)：147-155.
116.赵宪文.中国林业遥感发展中应该关注的几个问题[J].林业科学,2009,45(8)：135-140.
117.赵英时.遥感应用分析原理与方法[M].北京：科学出版社,2006：1-17.
118.周成虎,骆剑承,杨哓梅,杨存建.遥感影像地学理解与分析[M].北京：科学出版社,2003：1-63.
119.周成虎,骆剑承.高分辨率卫星遥感影像地学计算[M].北京：科学技术出版社,2009：27-173.
120.周启鸣,刘学军.数字地形分析[M].北京：科学出版社,2006：1-51.
121.周前祥,敬忠良,姜世忠.多源遥感影像信息融合研究现状及展望[J].宇航学报,2002,23(5)：89-94.
122.周润松,葛榜军.高分辨率商业遥感卫星现状与发展[J].卫星应用,2008,16(2)：47-52.
123.朱长青,史文中.空间分析建模与原理[M].北京：科学出版社,2006：43-50.
124.朱国宾.面向多分辨率层次结构的遥感影像分析方法[J].武汉大学学报(信息科学版),2003,28(3)：315-320.
125.朱明,濮励杰,李建龙.遥感影像空间分辨率及粒度变化对城市景观割据分析的影响[J].生态学报,2008,28(6)：2753-2763.
126.朱庆,李海峰,杨晓霞.遥感信息聚集服务的多层次语义约束模型[J].武汉大学学报(信息科学版),2009(12)：1454-1457.
127. Alvarez R,Bonifaz R,Lunetta R S,Garcia C,Gomez G,Castro R,Bernal A,Cabrera A L. Multitemporal land-cover classification of Mexico using Landsat MSS imagery[J].International Journal of Remote Sensing,2003,24(12):2501-2514.
128. Atkinson P M.Selecting the spatial resolution of airborne MSS imagery for small-scale agricultural mapping[J].International Journal of Remote Sensing,1997,18(9):1903-1917.
129. Atkinson P M,Curran P J.Choosing an appropriate spatial resolution for remote sensing investigations[J].Photogrammetric Engineering and Remote Sensing,1997,63(12):1345-1351.
130. Baatz M, Schape A (2000). Multiresolution segmentation:an optimization approach for high quality multiscale image segmentation.In Angewandte Geographische Informationsverarbeitung Ⅻ. (Eds:Strobl,J. and Blaschke, T.), Beitrage zum AGIT-Symposium Salzburg 2000, Karhlsruhe, Herbert Wichmann Verlag,12-23.
131. Benz U C,Hofmann P,Willhauck QLingenfelder I,Heynen M.Multi-resolution,object-oriented fuzzy analysis of remote sensing data for GIS-ready information [J].Photogrammetry and Remote Sensing,2004,58(3/4):239-258.
132. Bian L,Walsh S J.Scale dependencies of vegetation and topography in a mountainous environment of Montana[J].The Professional Geographer,1993,45(1):1-11.
133. Blan L,Butler R.Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data[J].Photogrammetric Engineering & Remote Sensing,1999,65(1):73-84.
134. Brice C R,Frennema C L."Scene Analysis Using Regions", Artifical Inteligence Grpoup,Ⅰ,205-226, Fall 1970.
135. Cao C,Lam N S N.Understanding the scale and resolution effects in remote sensing and GIS.In:Quattrochi D A, Goodchild M F(eds).Scalein Remote Sensing and GIS.LEWIS Publishers,1997.57-72.
136. Castleman K R. Digital Image Processing[M].Pearson Education,1996.
137. Chavez P S,Berlin G L,Sowers L B.Statistical methods for selecting Landsat MSS ratios[J].Journal of Applied Photographic Engineering,1982,8(1):23-30.
138. Chen Zhong, Zhao Zhongmin, Yan DongMei, Chen Renxi. Multi-scale segmentation of the high resolution remote sensing image.http://earth.definiens.com.
139. Davis F W, Quattrochi D A, Ridd M K,Lam N S N,Walsh S J, Michaelsen J C,Franklin J,Stow D A, Johannsen C J,Johnston C A.Environmental analysis using integrated GIS and remotely sensed data: some research needs and priorities[J].Photogrammetric Engineering & Remote Sensing, 1991,57(6):689-697.
140. DeCola L Fractal analysys of a classified Landsat scene[J]. Photogrammetric Engineering and Remote Sensing,1989,55:601-610.
141.Definiens AG.Definiens Professional 5 Reference Book[M].Definiens AG,2006.
142. Definiens AG.Definiens Professional 5 User Guide[M].Definiens AG,2006.
143. Definiens Imaging.eCognition User Guide 4[M]. Definiens Imaging,2004.
144. Delcourt H R,Delcourt P A.Quatemary Landscape Ecology:Relevent scales in space and time[J]. Landscape Ecology,1988,3(2):23-44..
145.DigitalGlobe.2009.http://www.digitalglobe.com/file.php/793/StandardImagery-DS-STAND.pdf
146. Dubayah R.Estimating net solar radiation using Landsat thematic mapper and digital elevation data[J].Water Resources Research,1992,28(9),2469-2484.
147. Foody G M,Boyd D S,Cutler M E J.Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions[J]. Remote Sensing of Environment, 2003,85:463-474.
148. Gao Y, Kerle N, Mas J F, Navarrete A,Niemeyer I.Optimized Image segmentation and its effect on classification accuracy[C]. ITC, Spatial Data Quality,5th International symposium,2007.
149. Gao Y, Mas J F. A comparision of the performance of pixel based and object based classifications over images with various spatial resolutions [J].OnLine Journal of Earth Sciences,2008,2(1):27-35.
150. Gonzalez R C,Woods R E.Digital Image Processing(Second Edition)[M].USA:Pearson Education, 2002:567-642.
151. Goodchild M F,Sun GYang S.Development and test of an error model for categorical data[J].International Journal of Geographical Information Systems,1992,6 (2):87-104.
152. Gopal S, Woodcock C.Theory and methods for accuracy assement of thematic maps using fuzzy sets[J].Photogrammetric Engineering & Remote Sensing,1994,60(2):181-188.
153. Halld L, Llinas J.An introduction to multisensor data fusion[J].Proceedings of The IEEE,1997,85(1):1-23.
154. Hofmann P, Strobl J, Blaschke T.A method for adapting global image segmentation methods to images of different resolutions.In:Proceedings of the 2nd International Conference on Geographic Object-Based Image Analysis University of Calgary,Alberta,Canada,August 6-7,2008.
155. Hua Wu,Zhao Liangli.Scale issues in remote sensing:A review on analysis, processing and modeling[J].Sensors,2009,(9):1768-1793.
156. Jensen J R.Introductory Digital Image Processing:A Remote Sensing Perspective(Third Edition) [M].Prentice Hall.Upper Saddle River,NJ,2005:163-401.
157. Kang Wenxiong,Yang Qingqiang,Liang Runpeng.The comparative research on image segmentation algorithms[C].Firat International Workshop on Education Technology and Computer Science,2009.
158. Ke Yinghai,Quackenbush L J.Forest species classification and tree crown delineation using QuickBird imagery[C].ASPRS 2007 Annual Conference,Tampa, Florida May 7-11,2007.
159. Krawitz L. Earth Resources Program Scope and Information Needs. NASA-CR\| 141767, General Electric Co., Philadelphia,1974.
160. Lam N S N,Quattrochi D A.On the issues of scale, resolution, and fractal analysis in the mapping sciences[J].The Professional Geographer,1992,44(l):88-98.
161.Leica Geosystems.Erdas Field Guide[M].Leica Geosyatems,2006,403-405.
162.Leica Geosystems.Leica Photogrammetry Suite Project Manager[M].Leica Geosystems,2006.
163. Mandelbrot B B. How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science,1967,156:636-638.
164. Marceau D J, Gratton D J,Fournier R A Fortin J P.Remote sensing and the measurement of geographical entities in a forested environment.Part 2:The optimal spatial resolution[J].Remote Sensing of Environment,1994,49(2):105-117.
165. Marceau D J, Hay G J. Remote sensing contributions to the scale issue[J].Canadian Journal of Remote Sensing,1999,25(4):357-366.
166. Marceau D J, Howarth P M, Gratton D J.Remote sensing and the measurement of geographical entities in a forested environment, Part 1:The scale and spatial aggregation problem[J]. Remote Sensing of Environment,1994,49(2):93-104.
167. Maribeth H Price. Application of remote sensing to forest resource inventory and habitat Modeling[J].NASA NRA-98-OES-09,2000.
168. Markham B L,Townshend J R G.1981.Land cover classification accuracy as a function of sensor spatial resolution.Proceedings of the 15th International Symposium on Remote Sensing of Environment, Ann Arbor,MI,11-15 May (Ann Arbor:ERIM):1075-1090.
169. Martin BAATZ,Arno SCHAPE.Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[J]. Journal of the Indian Society of Remote Sensing,2008,36(2),189-201.
170. Martin BAATZ,Arno SCHAPE.Multiresolution segmentation:an optimization approach for high quality multi-scale image segmentation[J]. Remote Sensing of Environment,2008,112 (6):2839-2849.
171. McCarty H H,Hook J C,Knox D S.The measurement of association in industrial geography. Iowa City:Department of Geography,University of Iowa,1956.
172. Meinel G,Neubert M.A comparison of segmentation programs for high resolution remote sensing data[C].International Society for Photogrammetry and Remote Sensing PS Ths9:Uncertainty, Consistency and Accuracy of Data and Imagery,2004.
173.NCGIA.NCIGA Research Initiatives, http://www.ncgia.ucsb.edu/
174.Neubert M,Herold H.Assessment of remote sensing image segmentation quality[C]. International Society for Photogrammetry and Remote Sensing Commission Ⅳ,Working Group Ⅳ/3,2008.
175. Openshaw S.The Modifiable Areal Unit Problem.Norwich:Geo Books,1984:3-38.
176. Peuquet D J.It's about time:A conceptual framework for the representation of temporal dynamics in geographic information systems[J]. Annals of the Association of American Geographers, 1994,84(3):441-461.
177. Pohl C,Van Genderen J L.Multisensor image fusion in remote sensing:concept,methods and applications [J]. International Journal of Remote Sensing,1998,9(5):823-854.
178. Raffy M.Change of scale theory:A capital challenge for space observation of earth[J].International Journal of Remote Sensing[J].1994,15(12):2353-2357.
179. Reeder D H.Topographic correction of satellite images theory and application.Hanover,New Hampshire:Dartmouth College,2002.
180. Spotimage.2009.Spot Images, http://www.spotimage.com/.
181. Stehman S V. Estimating the kappa coefficient and its variance under stratified random sampling [J].Photogrammetric Engineering and Remote Sensing,1996,62(4):401-407.
182. Tao C V, Hu Y.A comprehensive study of the rational function model for photogrammetric processing[J].Photogrammetric Engineering & Remote Sensing,2001,67(12):1347-1357.
183. Teillet P M,Guindon B,Goodenoug G.On the slope-aspect correction of multispeetral scanner data[J].Canadian Journal of Remote Sensing,1982,8(2):84-106.
184.Tian J,Chen D M.Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition[J].International Journal of Remote Sensing,2007,28(20):4625-4644.
185. Tobler W R.A computer movie simulating urban growth in the Detroit region[J].Economic Geography,1970,46(2):234-240.
186. White F E. Data fusion lexicon.Joint Directors of Laboratories,Technical Panel for C3, Data Fusion Sub-panel,Naval Ocean Systems Center,San diego,CA,USA,1987.
187. Woodcock C E,Gopal S.Fuzzy set theory and thematic maps:Accuracy assessment and area estimation[J].International Journal of GIS,2000,14(2):153-172.
188. Woodcock C E,Jupp D L B,Strahler A H.The use of variograms in remote sensing Ⅰ:Scene models and simulated images[J]. Remote Sensing of Environment,1988,25:323-348.
189. Woodcock C E,Strahler A H.The factor of scale in remote sensing[J].Remote Sensing of Environment,1987,21(3):311-332.
190. Woodcock C E,Strahler A H,Jupp D L B.The use of variograms in remote sensing Ⅱ:Real digital images[J].Remote Sensing of Environment,1988,25:349-379.
191. Wu C,Murray A T.Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment,2003,84(4):493-505.
192. Zhang Y.A new merging method and its spectral and spatial effects[J].International Journal of Remote Sensing,1999,20(10):2003-2014.
193. Zhang Y.Understanding image fusion[J].Photogrammetric Engineering & Remote Sensing,2004,70(6):657-661.
194. Zhang Y Z.Evaluation and comparision of different segmentation algorithms[J]. Pattern Recognition Letters,1997,18(10):963-974.
195. Zhang Z,Blum R S.A categorization of multiscale-decomposition-based image fusion scheme with a performance study for a digital camera application [J],Proceedings of the IEEE,2004,87(8):1315-1326.
196. Zucker S W.Digital Picture Processing[M].New York:Academic Press,1976:382-399.
2.柏延臣,王劲峰.遥感数据专题分类不确定性评价研究：进展、问题与展望[J].地球科学进展,2005,20(11)：1218-1225.
3.陈春雷,武刚.遥感影像中像素级融合方法的评价研究[J].浙江林业科技,2009,29(4)：59-64.
4.陈钢,欧阳明,俞帆,赵逸云,肖楠楠.物质表面图像的信息维研究[J].云南大学学报(自然科学版),2000,22(2)：109-112.
5.陈巧,陈永富.QuickBird遥感数据监测植被覆盖度的研究[J].林业科学研究,2005,18(4)：375-380.
6.陈秋晓,骆剑承,周成虎,郑江,鲁学军,沈占锋.基于多特征的遥感影像分类方法[J].遥感学报,2004,8(3)：239-245.
7.陈述彭,程维明.世界森林的数字地球监测[J].遥感学报,2001,5(5)：321-326.
8.陈述彭,赵英时.遥感地学分析[M].北京：测绘出版社,1990：5-27.
9.陈述彭.地球信息科学[M].北京：高等教育出版社,2007：1-147.
10.陈忠,赵忠明.基于区域生长的多尺度遥感图像分割算法[J].计算机工程与应用,2005,41(35)：7-9.
11.承继成,郭华东,史文中.遥感数据的不确定性问题[M].北京：科学出版社,2004：1-29.
12.戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析[M].北京：清华大学出版社,2004：186-186.
13.杜世宏,王桥,秦其明.空间关系模糊描述与组合推理[M].北京：科学出版社,2007：203-221.
14.段四波,阎广建.山区遥感图像地形校正模型研究综述[J].北京师范大学学报(自然科学版),2007,43(3)：362-366.
15.冯种葵,叶晓端Landsat 7卫星快速格式数据产品[J].遥感技术与应用,2000,15(4)：270-273.
16.付卓,胡吉平,谭衢霖,刘正军.遥感应用分析中影像分割方法[J].遥感技术与应用,2006,21(5)：456-462.
17.高永年,张万昌.遥感影像地形校正研究进展及其比较实验[J].地理研究,2008,27(2)：467-476.
18.关元秀,程晓阳.高分辨率卫星影像处理指南[M].北京：科学出版社,2008：141-221.
19.关泽群,刘继琳.遥感图像解译[M].武汉：武汉大学出版社,2007：1-14.
20.国家遥感中心.地球空间信息科学技术进展[M].北京：电子工业出版社,2009：79-163.
21.郭雷,李晖晖,鲍永生.图像融合[M].北京：电子工业出版社,2008：228-248.
22.郭庆胜,杜晓初,闫卫阳.地理空间推理[M].北京：科学出版社,2006：205-213.
23.韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京：清华大学出版社,2006：1-13.
24.韩鹏,龚建雅,李志林.基于信息熵的遥感分类最优空间尺度选择方法[J].武汉大学学报(信息科学版),2008,33(7)：676-679.
25.韩鹏,龚建雅.遥感尺度选择问题研究进展[J].遥感信息,2008,(1)：96-99.
26.何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学,2009,29(1)：106-109.
27.胡兴树,龚建雅,潘建平.当代遥感技术的现状和发展趋势[J].武汉大学学报(工学版),2003,36(3A)：195-198.
28.黄慧萍,吴炳芳.地物大小、对象尺度、影像分辨率的关系分析[J].遥感技术与应用,2006,21(3)：244-248.
29.黄慧萍,吴炳方.基于区域合并影像分割技术的多尺度地表景观分析[J].地理科学进展,2004,23(3)：9-15.
30.贾海峰,刘雪华.环境遥感原理与应用[M].北京：清华大学出版社,2006：112-141.
31.赖震刚,王继.利用ERDAS IMAGINE进行影像的几何精校正[J].现代测绘,2003,26(2)：38-40.
32.李春华,徐涵秋.高分辨率遥感图像融合的光谱保真问题[J].地球信息科学,2008,10(4)：620-526.
33.李德仁.论21世纪遥感与GIS的发展[J].武汉大学学报(信息科学版),2003,28(2)：127-131.
34.李君,范文义,李明泽.线性混合光谱模型在多光谱遥感影像分类中的应用[J].东北林业大学学报,2008,36(8)：45-48.
35.李俊杰,何隆华,戴锦芳,李金莲.遥感影像像元级融合方法与试验评价分析[J].地球信息科学,2008,10(1)：128-134.
36.李霖,吴凡.空间数据多尺度表达模型及其可视化[M].北京：科学出版社,2005：1-53.
37.李霖,应申.空间尺度基础性问题研究[J].武汉大学学报(信息科学版),2005,30(3)：199-203.
38.李明峰,冯宝红,刘三枝.GPS定位技术及其应用[M].北京：国防工业出版社,2006：18-37.
39.李爽,姚静.基于分形的DEM数据不确定性研究[M].北京：科学出版社,2007：69-89.
40.李爽成,蔡运龙.地理尺度转换若干问题的初步探讨[J].地理研究,2005,24(1)：11-18.
41.李素.TM/ETM+遥感影像混合像元分解及其应用研究[M].北京：中国农业大学出版社,2009：6-16.
42.李小文.定量遥感的发展与创新[J].河南大学学报,2005,35(4)：49-56.
43.李征航,黄劲松.GPS测量与数据处理[M].武昌：武汉大学出版社,2007：167-171.
44.梁顺林著,范闻捷等译.定量遥感[M].北京：科学出版社,2009：169-178.
45.刘凯,秦耀辰.论地理信息的尺度特性[J].地理与地理信息科学,2010,26(2)：1-5.
46.刘凯,任星,秦耀辰.地理信息语义尺度及其变换机制问题研究[J].河南大学学报(自然科学版),2010,40(3)：261-266.
47.刘明德,林杰斌.地理信息系统GIS理论与实务[M].北京：清华大学出版社,2006：182-210.
48.刘仁钊,廖文峰.遥感图像分类应用研究综述[J].地理空间信息,2005,3(5)：11-13.
49.刘贤赵,张安定,李嘉竹.地理学数学方法[M].北京：科学出版社,2009：1-75.
50.刘勇,刘悦翠,王周.SPOT5卫星影像在森林资源规划设计调查中的应用[J].安徽农业科学,2007,35(27)：8552-8553
51.栾庆祖,刘慧平,肖志强.遥感影像的正射校正方法比较[J].遥感技术与应用,2007,22(6)：743-747.
52.罗忠.多源遥感数据融合的现状[J].测试技术学报,1999,13(1)：32-38.
53.吕一河,博伯杰.生态学中的尺度及尺度转换方法[J].生态学报,2001,21(12)：2096-2105.
54.马蔼乃.理论地理科学与哲学：复杂性科学理论[M].北京：高等教育出版社,2007：88-191.
55.马克明,祖元刚.兴安落叶松种群格局的分性特征一信息维数[J].生态学报,2000,20(2)：187-192.
56.苗东升.系统科学大学讲稿[M].北京：中国人民大学出版社,2007：338-354.
57.苗东升.系统科学精要(第2版)[M].北京：中国人民大学出版社,2006：197-204.
58.那彦,焦李成.基于多分辨率分析理论的图像融合方法[M].西安：西安电子科技大学出版社,2007：83-172.
59.年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择—以云岭中部山区为例[J].云南地理环境研究,2004,16(2)：18-21.
60.潘玉君,武友德.地理科学导论[M].北京：科学出版社,2009：73-83.
61.彭望琭等.遥感概论[M].北京：高等教育出版社,2003：1-24.
62.钱乐祥.遥感数字影像处理与地理特征提取[M].北京：科学出版社,2004：183-221.
63.邵景安,陈兰,李阳兵,魏朝富,谢德体.未来区域土地利用驱动力研究的重要命题：尺度依赖[J].资源科学,2008,30(1)：58-63.
64.沈润平,王人潮,赵小敏.最佳特征影像组合选择指数的研究[J].华南农业大学学报(自然科学版),2002,23(2)：18-21.
65.史榕,许惠平,陈华根.遥感影像数据融合技术及融合质量评价研究[J].鲁东大学学报(自然科学版),2008,24(2)：130-134.
66.史文中,王树良.GIS之属性不确定性研究[J].中国图形图像学报,2001,6(9)：918-923.
67.苏理宏,李小文,黄裕霞.遥感尺度问题研究进展[J].地球科学进展,2001,16(4)：544-548.
68.孙家抦.遥感原理与应用[M].武汉：武汉大学出版社,2005：98-141.
69.孙小芳,卢健,孙依斌.城市绿地多尺度分割提取及景观分析[J].遥感信息,2006,(2)：31-33.
70.谭兵,邢帅,徐青,李建胜.SPOT5超模式数据处理技术研究[J].遥感技术与应用,2004,19(4)：249-252.
71.谭炳香.新世纪遥感与林业信息需求[J].遥感信息,2002,(2)：32-37.
72.谭皓,万彦辉.GPS定位的坐标转换[J].导航与控制,2004,3(3)：6-9.
73.谭衢霖,刘正军,沈伟.一种面向对象的遥感影像多尺度分割方法[J].北京交通大学学报,2007,31(4)：111-119.
74.谭永生,沈掌泉,贾春燕,王新辉,邓劲松.中高分辨率遥感影像融合研究[J].遥感技术与应用,2007,22(4)：536-542.
75.王爱民.地理学思想史[M].北京：科学出版社,2010：262-281.
76.王倩,张起鹏.分形理论在遥感图像分类处理中的应用综述[J].河北遥感,2008,(4)：14-15.
77.王桥,毋河海.地图信息的分形描述与自动综合研究[M].武汉：武汉测绘科技大学出版社,1998：1-92.
78.王淑,王恒山,肖刚.多源遥感影像融合理论、技术和应用[J].微型电脑应用,2005,21(12)：1-5.
79.王艳慧,孟浩.GIS中地理要素多尺度表达间层次连通性的研究[J].湖南科技大学学报,2006,12(1)：59-63.
80.王政权.地统计学及在生态学中的应用[M].北京：科学出版社,1999：35-101.
81.汪自军,陈圣波,韩念龙,湛邵斌,吕航.地学尺度转换理论及方法研究[J].地理空间信息,2007,5(4)：60-63.
82.韦玉春,汤国安,杨昕.遥感数字图像处理教程[M].北京：科学出版社,2007：177-255.
83.魏克让,江聪世.空间数据的误差处理[M].北京：科学出版社,2005：9-24.
84.邬建国.景观生态学[M].北京：高等教育出版社,2007：25-158.
85.吴建平.分形理论在遥感中的应用[J].遥感技术与应用,1993,8(4)：23-27.
86.肖奥,陶舒,王晓爽,张彤欣.遥感融合方法分析与评价[J].首都师范大学学报(自然科学版),2007,28(4)：77-80.
87.肖笃宁,李秀珍,高峻,常禹,李团胜.景观生态学[M].北京：科学出版社,2007：17-59.
88.徐建华,岳文泽,谈文琦.城市景观格局尺度效应的空间规律—以上海中心城区为例[J].地理学报,2004,59(6)：1058-1067.
89.徐青,潭国光.遥感数据的分形测量[J].遥感学报,1998,2(3)：186-192.
90.徐青,张艳,耿则勋,刑帅,谭兵.遥感影像融合与分辨率增强技术[M].北京：科学技术出版 社,2007：68-128.
91.徐庆铃,彭世揆.TM影像地形辐射校正的应用研究--以广东九曲水林场为例[D].南京林业大学,2008：23-32.
92.许榕峰,徐涵秋.ETM+全色波段及其多光谱波段图像的融合应用[J].地球信息科学,2004,6(1)：99-103.
93.闫海文,褚衍东.多尺度地图空间相似关系基本问题研究[J].地理与地理信息科学,2009,25(4)：42-44.
94.杨晖,曲秀杰.图像分割方法综述[J].电脑开发与应用,2005,18(3)：21-23.
95.杨金红,尹球,顾松山,周宁,刘晓明.城区高光谱遥感数据假彩色波段组合研究[J].南京气象学院学报,2005,28(3)：289-295.
96.杨燕,田庆久.森林覆盖区山地遥感地形校正的方法研究[J].遥感信息,2008,(1)：22-26.
97.尹占娥.现代遥感导论[M].北京：科学出版社,2008：218-221.
98.游先祥.遥感原理及其在资源环境中的应用[M].北京：中国林业出版社,2003：1-12.
99.袁传武,柯尊胜,陈双田,吴翠,王珠娜,崔鸿侠.多源遥感数据融合原理与模型结构及应用[J].西南林学院学报,2007,27(5)：14-17.
100.詹勤,李德仁,眭海钢,张霞.一种遥感信息服务分类本体构建方法[J].武汉大学学报(信息科学版),2010,35(3)：343-346.
101.张超,王人潮,赵春江,李海霞,刘良云.山区高分辨率遥感影像地形辐射校正方法研究[J].浙江大学学报(农业与生命科学版),2006,32(6)：693-698.
102.张洪亮,倪绍祥,张军.国外遥感图像的地形归一化方法研究进展[J].遥感信息,2001,(3)：24-29.
103.张济忠.分形[M].北京：清华大学出版社,2006：7-79.
104.张景雄.空间信息的尺度、不确定性与融合[M].武汉大学出版社,2008：1-19.
105.张俊,王宝山,吕宝庆.面向对象高分辨率影像信息提取中的尺度效应研究[J].北京测绘,2010,(1)：11-14.
106.张俊,汪云甲,李妍,王行凤.一种面向对象的高分辨率影像最优分割尺度选择算法[J].科技导报,2009,27(21)：91-94.
107.张永生,巩丹超.高分辨率遥感卫星应用—成像模型、处理算法及应用技术[M].北京：科学出版社,2005：15-51.
108.张煜星,王祝雄.遥感技术在森林资源清查中的应用研究[M].北京：中国林业出版社,2007：1-14.
109.章毓晋.图象工程(上册)[M].北京：清华大学出版社,2006：365-387.
110.章毓晋.图象工程(中册)[M].北京：清华大学出版社,2006：73-105.
111.章毓晋.图象工程(下册)[M].北京：清华大学出版社,2007：201-227.
112.赵茂程,高素萍,潘一凡,郑加强.分形理论及其在林业中的应用与研究进展[J].世界林业研究,2002,15(2)：28-34.
113.赵书河.多源遥感影像融合技术与应用[M].南京：南京大学出版社,2008：1-35.
114.赵文武,傅伯杰,陈利顶.尺度推绎中的几点基本问题[J].地球科学进展,2002,17(6)：905-911.
115.赵宪文,李崇贵,斯林,袁凯先,田永林.基于信息技术的森林资源调查新体系[J].北京林业大学学报,2002,24(5/6)：147-155.
116.赵宪文.中国林业遥感发展中应该关注的几个问题[J].林业科学,2009,45(8)：135-140.
117.赵英时.遥感应用分析原理与方法[M].北京：科学出版社,2006：1-17.
118.周成虎,骆剑承,杨哓梅,杨存建.遥感影像地学理解与分析[M].北京：科学出版社,2003：1-63.
119.周成虎,骆剑承.高分辨率卫星遥感影像地学计算[M].北京：科学技术出版社,2009：27-173.
120.周启鸣,刘学军.数字地形分析[M].北京：科学出版社,2006：1-51.
121.周前祥,敬忠良,姜世忠.多源遥感影像信息融合研究现状及展望[J].宇航学报,2002,23(5)：89-94.
122.周润松,葛榜军.高分辨率商业遥感卫星现状与发展[J].卫星应用,2008,16(2)：47-52.
123.朱长青,史文中.空间分析建模与原理[M].北京：科学出版社,2006：43-50.
124.朱国宾.面向多分辨率层次结构的遥感影像分析方法[J].武汉大学学报(信息科学版),2003,28(3)：315-320.
125.朱明,濮励杰,李建龙.遥感影像空间分辨率及粒度变化对城市景观割据分析的影响[J].生态学报,2008,28(6)：2753-2763.
126.朱庆,李海峰,杨晓霞.遥感信息聚集服务的多层次语义约束模型[J].武汉大学学报(信息科学版),2009(12)：1454-1457.
127. Alvarez R,Bonifaz R,Lunetta R S,Garcia C,Gomez G,Castro R,Bernal A,Cabrera A L. Multitemporal land-cover classification of Mexico using Landsat MSS imagery[J].International Journal of Remote Sensing,2003,24(12):2501-2514.
128. Atkinson P M.Selecting the spatial resolution of airborne MSS imagery for small-scale agricultural mapping[J].International Journal of Remote Sensing,1997,18(9):1903-1917.
129. Atkinson P M,Curran P J.Choosing an appropriate spatial resolution for remote sensing investigations[J].Photogrammetric Engineering and Remote Sensing,1997,63(12):1345-1351.
130. Baatz M, Schape A (2000). Multiresolution segmentation:an optimization approach for high quality multiscale image segmentation.In Angewandte Geographische Informationsverarbeitung Ⅻ. (Eds:Strobl,J. and Blaschke, T.), Beitrage zum AGIT-Symposium Salzburg 2000, Karhlsruhe, Herbert Wichmann Verlag,12-23.
131. Benz U C,Hofmann P,Willhauck QLingenfelder I,Heynen M.Multi-resolution,object-oriented fuzzy analysis of remote sensing data for GIS-ready information [J].Photogrammetry and Remote Sensing,2004,58(3/4):239-258.
132. Bian L,Walsh S J.Scale dependencies of vegetation and topography in a mountainous environment of Montana[J].The Professional Geographer,1993,45(1):1-11.
133. Blan L,Butler R.Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data[J].Photogrammetric Engineering & Remote Sensing,1999,65(1):73-84.
134. Brice C R,Frennema C L."Scene Analysis Using Regions", Artifical Inteligence Grpoup,Ⅰ,205-226, Fall 1970.
135. Cao C,Lam N S N.Understanding the scale and resolution effects in remote sensing and GIS.In:Quattrochi D A, Goodchild M F(eds).Scalein Remote Sensing and GIS.LEWIS Publishers,1997.57-72.
136. Castleman K R. Digital Image Processing[M].Pearson Education,1996.
137. Chavez P S,Berlin G L,Sowers L B.Statistical methods for selecting Landsat MSS ratios[J].Journal of Applied Photographic Engineering,1982,8(1):23-30.
138. Chen Zhong, Zhao Zhongmin, Yan DongMei, Chen Renxi. Multi-scale segmentation of the high resolution remote sensing image.http://earth.definiens.com.
139. Davis F W, Quattrochi D A, Ridd M K,Lam N S N,Walsh S J, Michaelsen J C,Franklin J,Stow D A, Johannsen C J,Johnston C A.Environmental analysis using integrated GIS and remotely sensed data: some research needs and priorities[J].Photogrammetric Engineering & Remote Sensing, 1991,57(6):689-697.
140. DeCola L Fractal analysys of a classified Landsat scene[J]. Photogrammetric Engineering and Remote Sensing,1989,55:601-610.
141.Definiens AG.Definiens Professional 5 Reference Book[M].Definiens AG,2006.
142. Definiens AG.Definiens Professional 5 User Guide[M].Definiens AG,2006.
143. Definiens Imaging.eCognition User Guide 4[M]. Definiens Imaging,2004.
144. Delcourt H R,Delcourt P A.Quatemary Landscape Ecology:Relevent scales in space and time[J]. Landscape Ecology,1988,3(2):23-44..
145.DigitalGlobe.2009.http://www.digitalglobe.com/file.php/793/StandardImagery-DS-STAND.pdf
146. Dubayah R.Estimating net solar radiation using Landsat thematic mapper and digital elevation data[J].Water Resources Research,1992,28(9),2469-2484.
147. Foody G M,Boyd D S,Cutler M E J.Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions[J]. Remote Sensing of Environment, 2003,85:463-474.
148. Gao Y, Kerle N, Mas J F, Navarrete A,Niemeyer I.Optimized Image segmentation and its effect on classification accuracy[C]. ITC, Spatial Data Quality,5th International symposium,2007.
149. Gao Y, Mas J F. A comparision of the performance of pixel based and object based classifications over images with various spatial resolutions [J].OnLine Journal of Earth Sciences,2008,2(1):27-35.
150. Gonzalez R C,Woods R E.Digital Image Processing(Second Edition)[M].USA:Pearson Education, 2002:567-642.
151. Goodchild M F,Sun GYang S.Development and test of an error model for categorical data[J].International Journal of Geographical Information Systems,1992,6 (2):87-104.
152. Gopal S, Woodcock C.Theory and methods for accuracy assement of thematic maps using fuzzy sets[J].Photogrammetric Engineering & Remote Sensing,1994,60(2):181-188.
153. Halld L, Llinas J.An introduction to multisensor data fusion[J].Proceedings of The IEEE,1997,85(1):1-23.
154. Hofmann P, Strobl J, Blaschke T.A method for adapting global image segmentation methods to images of different resolutions.In:Proceedings of the 2nd International Conference on Geographic Object-Based Image Analysis University of Calgary,Alberta,Canada,August 6-7,2008.
155. Hua Wu,Zhao Liangli.Scale issues in remote sensing:A review on analysis, processing and modeling[J].Sensors,2009,(9):1768-1793.
156. Jensen J R.Introductory Digital Image Processing:A Remote Sensing Perspective(Third Edition) [M].Prentice Hall.Upper Saddle River,NJ,2005:163-401.
157. Kang Wenxiong,Yang Qingqiang,Liang Runpeng.The comparative research on image segmentation algorithms[C].Firat International Workshop on Education Technology and Computer Science,2009.
158. Ke Yinghai,Quackenbush L J.Forest species classification and tree crown delineation using QuickBird imagery[C].ASPRS 2007 Annual Conference,Tampa, Florida May 7-11,2007.
159. Krawitz L. Earth Resources Program Scope and Information Needs. NASA-CR\| 141767, General Electric Co., Philadelphia,1974.
160. Lam N S N,Quattrochi D A.On the issues of scale, resolution, and fractal analysis in the mapping sciences[J].The Professional Geographer,1992,44(l):88-98.
161.Leica Geosystems.Erdas Field Guide[M].Leica Geosyatems,2006,403-405.
162.Leica Geosystems.Leica Photogrammetry Suite Project Manager[M].Leica Geosystems,2006.
163. Mandelbrot B B. How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science,1967,156:636-638.
164. Marceau D J, Gratton D J,Fournier R A Fortin J P.Remote sensing and the measurement of geographical entities in a forested environment.Part 2:The optimal spatial resolution[J].Remote Sensing of Environment,1994,49(2):105-117.
165. Marceau D J, Hay G J. Remote sensing contributions to the scale issue[J].Canadian Journal of Remote Sensing,1999,25(4):357-366.
166. Marceau D J, Howarth P M, Gratton D J.Remote sensing and the measurement of geographical entities in a forested environment, Part 1:The scale and spatial aggregation problem[J]. Remote Sensing of Environment,1994,49(2):93-104.
167. Maribeth H Price. Application of remote sensing to forest resource inventory and habitat Modeling[J].NASA NRA-98-OES-09,2000.
168. Markham B L,Townshend J R G.1981.Land cover classification accuracy as a function of sensor spatial resolution.Proceedings of the 15th International Symposium on Remote Sensing of Environment, Ann Arbor,MI,11-15 May (Ann Arbor:ERIM):1075-1090.
169. Martin BAATZ,Arno SCHAPE.Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[J]. Journal of the Indian Society of Remote Sensing,2008,36(2),189-201.
170. Martin BAATZ,Arno SCHAPE.Multiresolution segmentation:an optimization approach for high quality multi-scale image segmentation[J]. Remote Sensing of Environment,2008,112 (6):2839-2849.
171. McCarty H H,Hook J C,Knox D S.The measurement of association in industrial geography. Iowa City:Department of Geography,University of Iowa,1956.
172. Meinel G,Neubert M.A comparison of segmentation programs for high resolution remote sensing data[C].International Society for Photogrammetry and Remote Sensing PS Ths9:Uncertainty, Consistency and Accuracy of Data and Imagery,2004.
173.NCGIA.NCIGA Research Initiatives, http://www.ncgia.ucsb.edu/
174.Neubert M,Herold H.Assessment of remote sensing image segmentation quality[C]. International Society for Photogrammetry and Remote Sensing Commission Ⅳ,Working Group Ⅳ/3,2008.
175. Openshaw S.The Modifiable Areal Unit Problem.Norwich:Geo Books,1984:3-38.
176. Peuquet D J.It's about time:A conceptual framework for the representation of temporal dynamics in geographic information systems[J]. Annals of the Association of American Geographers, 1994,84(3):441-461.
177. Pohl C,Van Genderen J L.Multisensor image fusion in remote sensing:concept,methods and applications [J]. International Journal of Remote Sensing,1998,9(5):823-854.
178. Raffy M.Change of scale theory:A capital challenge for space observation of earth[J].International Journal of Remote Sensing[J].1994,15(12):2353-2357.
179. Reeder D H.Topographic correction of satellite images theory and application.Hanover,New Hampshire:Dartmouth College,2002.
180. Spotimage.2009.Spot Images, http://www.spotimage.com/.
181. Stehman S V. Estimating the kappa coefficient and its variance under stratified random sampling [J].Photogrammetric Engineering and Remote Sensing,1996,62(4):401-407.
182. Tao C V, Hu Y.A comprehensive study of the rational function model for photogrammetric processing[J].Photogrammetric Engineering & Remote Sensing,2001,67(12):1347-1357.
183. Teillet P M,Guindon B,Goodenoug G.On the slope-aspect correction of multispeetral scanner data[J].Canadian Journal of Remote Sensing,1982,8(2):84-106.
184.Tian J,Chen D M.Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition[J].International Journal of Remote Sensing,2007,28(20):4625-4644.
185. Tobler W R.A computer movie simulating urban growth in the Detroit region[J].Economic Geography,1970,46(2):234-240.
186. White F E. Data fusion lexicon.Joint Directors of Laboratories,Technical Panel for C3, Data Fusion Sub-panel,Naval Ocean Systems Center,San diego,CA,USA,1987.
187. Woodcock C E,Gopal S.Fuzzy set theory and thematic maps:Accuracy assessment and area estimation[J].International Journal of GIS,2000,14(2):153-172.
188. Woodcock C E,Jupp D L B,Strahler A H.The use of variograms in remote sensing Ⅰ:Scene models and simulated images[J]. Remote Sensing of Environment,1988,25:323-348.
189. Woodcock C E,Strahler A H.The factor of scale in remote sensing[J].Remote Sensing of Environment,1987,21(3):311-332.
190. Woodcock C E,Strahler A H,Jupp D L B.The use of variograms in remote sensing Ⅱ:Real digital images[J].Remote Sensing of Environment,1988,25:349-379.
191. Wu C,Murray A T.Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment,2003,84(4):493-505.
192. Zhang Y.A new merging method and its spectral and spatial effects[J].International Journal of Remote Sensing,1999,20(10):2003-2014.
193. Zhang Y.Understanding image fusion[J].Photogrammetric Engineering & Remote Sensing,2004,70(6):657-661.
194. Zhang Y Z.Evaluation and comparision of different segmentation algorithms[J]. Pattern Recognition Letters,1997,18(10):963-974.
195. Zhang Z,Blum R S.A categorization of multiscale-decomposition-based image fusion scheme with a performance study for a digital camera application [J],Proceedings of the IEEE,2004,87(8):1315-1326.
196. Zucker S W.Digital Picture Processing[M].New York:Academic Press,1976:382-399.