遥感信息不确定性建模及其可视化表达研究
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摘要
客观世界本身内在的不确定性和人类对客观世界认知的局限性,是导致科学对客观真实的描述存在大量不确定性的主要原因。不确定性问题以其普遍性和现实性,成为当前研究领域中的一个难点和热点。
遥感的研究对象是空间实体,描述空间实体的空间数据中,包括与时间有关的位置数据和属性数据,都存在着大量不确定性,有的甚至严重影响产品的可靠性。数据质量是数据的核心,而数据质量的关键则是对不确定性的评估。
如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布是遥感信息不确定性研究的关键问题之一。传统的度量方法,例如误差矩阵和Kappa系数,直接在训练数据上估计分类模型的性能。而事实上这样做并不合适,不能将以训练样本集为基础的误差矩阵当作总分类精度的量测尺度,我们需要估计模型对于“样本外数据”的性能。因此我们提出将粗糙集理论作为度量遥感信息属性不确定性问题的应用框架,对样本区数据和分类影像中的像元、目标和影像整体等不同尺度上的空间对象进行属性不确定性度量,并以我国黄河三角洲地区的Landsat5TM遥感影像进行不确定性度量的实例分析。
由于混合像元而造成的影像不确定性属于遥感技术固有的不确定性特征,产生混合像元的原因则主要是遥感影像数据的栅格编码格式与传感器的空间分辨能力和空间尺度等因素。混合像元的存在限制了传统像元级遥感分类的精度,而这些分类方法中又都很少考虑整个影像内相邻像元或者该像元与周围像元间的关联关系,即空间数据的空间依赖或空间自相关特性。论文根据遥感影像的空间自相关特性,提出混合像元内部亚像元空间分布的模拟算法,利用像元内部基本组分的面积比信息,在亚像元尺度上模拟混合像元内部的地物空间分布,实现了进入像元内部,在亚像元级别上确定地物边界的目的。同时利用人工影像数据和合成影像数据对模拟算法的性能和效率在不同配置的硬件平台上进行了测试。
遥感信息中存在大量不确定性,如果只是仅仅对其进行不确定性程度上的数学度量,而忽略其在空间域和时间域上的分布特征,则很难完整准确地描述和理解遥感信息中的不确定性。从视觉的角度表现不确定性信息是不确定性建模的重要组成部分,不确定性可视化技术能够辅助用户探查原始数据的不确定性以及不确定性的大小、分布、空间结构和趋势,使用户简单明确地意识到数据不确定性的存在,以
The inherent uncertainties in the real world and the limitation of human cognition are primary factors of the abundance of uncertainty existing in the judgment of sciences to objectivities. Due to its universality and reality, the uncertainty problem is becoming a hotspot and a difficult issue in the present research fields.The research target in remote sensing is the spatial entity. An amount of uncertainties exist in the spatial data describing the spatial entity, including temporal-relative location data and attribute data, some of which may seriously distort the reliability of products. Data quality is the core of data, and the key of data quality is evaluating uncertainty.One of the key points in research of uncertainties in remote sensing is measuring and visualizing the degree and spatial distribution of uncertainty completely and accurately in the processing of remotely sensed image. In the classical fashions, e.g. error matrix and Kappa coefficient, the performance of the classification models is estimated directly on the training data. Whereas it is actually not appropriate. The error matrix based on the training data set can not be regarded as the measurement of overall accuracy of classification models, and these models' performance need to be evaluated on "out-of-sample-data" - data that have not been used in constructing the models. In this paper, we apply the rough sets theory as the application framework of measuring the attribute uncertainties in remote sensing information, and several measures are proposed for assessing the attribute uncertainties in sample area data and different spatial objects based on the scale of pixel, landcover class and the whole image in classified remotely sensed imagery. These measurements could measure effectively attribute uncertainties and facilitate to trace the propagation of error and uncertainties in classified remotely sensed data. Subsequently, the remotely sensed imagery of Landsat 5 TM located at the Yellow River Delta is utilized as a case study of uncertainty analysis.Remotely sensed images often contain a combination of both pure and mixed pixels. The presence of mixed pixels is primarily due to the raster encoding format of remote
sensing data, characteristic of the sensor's response to radiation, spatial resolution of the sensor, and spatial scale. The uncertainty in the imagery resulted from the aforementioned reflects the inherent characteristic of uncertainty kept in the technique of remote sensing. The existence of mixed pixels restricts the accuracy of classical classifiers of remotely sensed information based on the level of pixel, and these classifiers rarely consider the relationship of spatial conjunction between the neighboring pixels in the whole image, in other words, the spatial dependency of spatial data or spatial autocorrelation. In this paper, a novel simulating algorithm for identifying the spatial distribution of subpixels in a mixed pixel is proposed based on the assumption of spatial autocorrelation. Cooperating with the information on the proportions of every endmember component presented within a pixel derived from a soft classification, the algorithm simulates the spatial distribution of landcovers within the mixed pixel, and implements crossing through the pixel and identifying the boundary between the field objects at the subpixel scale. Subsequently, the performance and efficiency of the simulating algorithm is tested on different configuration of hardware platforms with the artificial imagery and synthetic imagery respectively.Large amount of uncertainties exist in the remote sensing information. If only mathematics measurement on the uncertainty degree of the remote sensing information is utilized, while the distribution characteristics in the temporal and spatial domains is ignored, the uncertainties in remote sensing information will be difficult to be completely and accurately described and understood. Representing uncertainty information from the point of view of visual sensation is an important section of modelling uncertainty. The uncertainty visualizing technology will help the users to explore uncertainty of the raw data and the size, distribution, special structure and tendency of the uncertainty. Then, the existence of uncertainty in data and the effects of uncertainty on the final decision would be simply and exactly felt by the users and products of remote sensing can be better understood and employed. In this paper, methods and technologies of uncertainty visualization are discussed and applied by the compartmentalization of static and dynamic visualized variable and feature visualization. The technique of the parallel coordinates is applied with emphasis in the feature space of remote sensing information to visualize the imagery's character of spectral and uncertainty, and then some visualizing examples of uncertainty measurements in the classified remotely sensed imagery are provided.At last, the design and the flow of data handling of the software UnVis for analyzing uncertainty in the remote sensing information are described, and the performance of the two finished functional modules of attribute uncertainty measuring and subpixel spatial distribution simulating is tested.
遥感的研究对象是空间实体,描述空间实体的空间数据中,包括与时间有关的位置数据和属性数据,都存在着大量不确定性,有的甚至严重影响产品的可靠性。数据质量是数据的核心,而数据质量的关键则是对不确定性的评估。
如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布是遥感信息不确定性研究的关键问题之一。传统的度量方法,例如误差矩阵和Kappa系数,直接在训练数据上估计分类模型的性能。而事实上这样做并不合适,不能将以训练样本集为基础的误差矩阵当作总分类精度的量测尺度,我们需要估计模型对于“样本外数据”的性能。因此我们提出将粗糙集理论作为度量遥感信息属性不确定性问题的应用框架,对样本区数据和分类影像中的像元、目标和影像整体等不同尺度上的空间对象进行属性不确定性度量,并以我国黄河三角洲地区的Landsat5TM遥感影像进行不确定性度量的实例分析。
由于混合像元而造成的影像不确定性属于遥感技术固有的不确定性特征,产生混合像元的原因则主要是遥感影像数据的栅格编码格式与传感器的空间分辨能力和空间尺度等因素。混合像元的存在限制了传统像元级遥感分类的精度,而这些分类方法中又都很少考虑整个影像内相邻像元或者该像元与周围像元间的关联关系,即空间数据的空间依赖或空间自相关特性。论文根据遥感影像的空间自相关特性,提出混合像元内部亚像元空间分布的模拟算法,利用像元内部基本组分的面积比信息,在亚像元尺度上模拟混合像元内部的地物空间分布,实现了进入像元内部,在亚像元级别上确定地物边界的目的。同时利用人工影像数据和合成影像数据对模拟算法的性能和效率在不同配置的硬件平台上进行了测试。
遥感信息中存在大量不确定性,如果只是仅仅对其进行不确定性程度上的数学度量,而忽略其在空间域和时间域上的分布特征,则很难完整准确地描述和理解遥感信息中的不确定性。从视觉的角度表现不确定性信息是不确定性建模的重要组成部分,不确定性可视化技术能够辅助用户探查原始数据的不确定性以及不确定性的大小、分布、空间结构和趋势,使用户简单明确地意识到数据不确定性的存在,以
The inherent uncertainties in the real world and the limitation of human cognition are primary factors of the abundance of uncertainty existing in the judgment of sciences to objectivities. Due to its universality and reality, the uncertainty problem is becoming a hotspot and a difficult issue in the present research fields.The research target in remote sensing is the spatial entity. An amount of uncertainties exist in the spatial data describing the spatial entity, including temporal-relative location data and attribute data, some of which may seriously distort the reliability of products. Data quality is the core of data, and the key of data quality is evaluating uncertainty.One of the key points in research of uncertainties in remote sensing is measuring and visualizing the degree and spatial distribution of uncertainty completely and accurately in the processing of remotely sensed image. In the classical fashions, e.g. error matrix and Kappa coefficient, the performance of the classification models is estimated directly on the training data. Whereas it is actually not appropriate. The error matrix based on the training data set can not be regarded as the measurement of overall accuracy of classification models, and these models' performance need to be evaluated on "out-of-sample-data" - data that have not been used in constructing the models. In this paper, we apply the rough sets theory as the application framework of measuring the attribute uncertainties in remote sensing information, and several measures are proposed for assessing the attribute uncertainties in sample area data and different spatial objects based on the scale of pixel, landcover class and the whole image in classified remotely sensed imagery. These measurements could measure effectively attribute uncertainties and facilitate to trace the propagation of error and uncertainties in classified remotely sensed data. Subsequently, the remotely sensed imagery of Landsat 5 TM located at the Yellow River Delta is utilized as a case study of uncertainty analysis.Remotely sensed images often contain a combination of both pure and mixed pixels. The presence of mixed pixels is primarily due to the raster encoding format of remote
sensing data, characteristic of the sensor's response to radiation, spatial resolution of the sensor, and spatial scale. The uncertainty in the imagery resulted from the aforementioned reflects the inherent characteristic of uncertainty kept in the technique of remote sensing. The existence of mixed pixels restricts the accuracy of classical classifiers of remotely sensed information based on the level of pixel, and these classifiers rarely consider the relationship of spatial conjunction between the neighboring pixels in the whole image, in other words, the spatial dependency of spatial data or spatial autocorrelation. In this paper, a novel simulating algorithm for identifying the spatial distribution of subpixels in a mixed pixel is proposed based on the assumption of spatial autocorrelation. Cooperating with the information on the proportions of every endmember component presented within a pixel derived from a soft classification, the algorithm simulates the spatial distribution of landcovers within the mixed pixel, and implements crossing through the pixel and identifying the boundary between the field objects at the subpixel scale. Subsequently, the performance and efficiency of the simulating algorithm is tested on different configuration of hardware platforms with the artificial imagery and synthetic imagery respectively.Large amount of uncertainties exist in the remote sensing information. If only mathematics measurement on the uncertainty degree of the remote sensing information is utilized, while the distribution characteristics in the temporal and spatial domains is ignored, the uncertainties in remote sensing information will be difficult to be completely and accurately described and understood. Representing uncertainty information from the point of view of visual sensation is an important section of modelling uncertainty. The uncertainty visualizing technology will help the users to explore uncertainty of the raw data and the size, distribution, special structure and tendency of the uncertainty. Then, the existence of uncertainty in data and the effects of uncertainty on the final decision would be simply and exactly felt by the users and products of remote sensing can be better understood and employed. In this paper, methods and technologies of uncertainty visualization are discussed and applied by the compartmentalization of static and dynamic visualized variable and feature visualization. The technique of the parallel coordinates is applied with emphasis in the feature space of remote sensing information to visualize the imagery's character of spectral and uncertainty, and then some visualizing examples of uncertainty measurements in the classified remotely sensed imagery are provided.At last, the design and the flow of data handling of the software UnVis for analyzing uncertainty in the remote sensing information are described, and the performance of the two finished functional modules of attribute uncertainty measuring and subpixel spatial distribution simulating is tested.
引文
[1] Aboelghar, M., Tateishi, R. & Tsolmon, R. Land-cover sub-pixel classification using linear mixture model on Landsat ETM+ data in EGYPT, the 23rd Asian Conference on Remote Sensing, November 25-29, 2002, Kathmandu, Nepal.
[2] Agumya, A. & Hunter, G. J. Responding to the consequences of uncertainty in geographical data, International Journal of Geographical Information Science, 2002, 16(5): 405-417.
[3] Aplin, P. & Atkinson, P. M. Sub-pixel land cover mapping for per-field classification. International Journal of Remote Sensing, 2001, 22(14): 2853-2858.
[4] Aplin, P., Atkinson, P. M., Curran, P. J. Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment, 1999, 68: 206-216.
[5] Appleton, K. Representing uncertainty in geovisualisation: a research agenda. European Science Foundation EURESCO conference "Geovisualisation", in Kolymbari, Crete, 13th-18th March 2004.
[6] Atkinson, P. M. Mapping sub-pixel vector boundaries from remotely sensed images. In Proceedings of GISRUK '96. University of Kent, Canterbury, UK, 29-41. In: Kemp Z. Edi: Innovations in GIS 4, London: Taylor and Francis, 1997, pp. 166-180.
[7] Atkinson, P. M. Super-resolution target mapping from soft classified remotely sensed imagery. In: Proceedings of the 5th International Conference on GeoComputation. Leeds, UK, University of Leeds. CD-ROM. 2001. http://www.geocomputation.org/2001/papers/atkinson.pdf
[8] Bastin, L. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixeis. International Journal of Remote Sensing. 1997, 18(17): 3629-3648.
[9] Bastin, L., Wood, J. & Fisher, P. F. Visualization of fuzzy spatial information in spatial decision-making. In: Lowell, K., Jaton, A. (Eds.), Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources. Ann Arbor Press, Chelsea, MI, 1999a. pp. 151-156.
[10] Bastin, L., Wood, J. & Fisher, P.F. Visualizing uncertainty in fuzzy thematic classi.cations of multispectral satellite imagery. In: Shi, W., Goodchild, M. F., Fisher, P. F. (Eds.), Proceedings of the International Symposium on Spatial Data Quality'99. Polytechnic University, Hong Kong, 1999b. pp. 243-252.
[11] Bastin, L., Fisher, P. F., & Wood, J. Visualizing uncertainty in multi-spectral remotely sensed imagery, Computers & Geosciences, 2002, 28(3): 337-350.
[12] Blakney, W. G. G. Accuracy standards for topographic mapping. Photogrammetric Engineering and Remote Sensing, 1968, 34(10): 1040-1042.
[13] Burrough, P. A. & McDonnell, R. A. Principles of geographical information systems. Oxford University Press, 1998.
[14] Carlo, W. D. Exploring multi-dimensional remote sensing data with a virtual reality system, Geographical & Environmental Modelling, 2000, 4(1):7-20.
[15] Carmel, Y., Dean, D.J., Flather, C.H. Combining location and classification error sources for estimating multi-temporal database accuracy, PE&RS, 2001, 67(7):865-872.
[16] Chen, P.C. Developing a personal computer-based data visualization system using public domain software, SPIE's Electronic Imaging '99, January 27,1999, San Jose, California, USA.
[17] Congalton, R.G. & Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles of Practices. Lewis Publishers, 1999.
[18] Couclelis, H. Living with uncertainty: GIS and the limits of geographic knowledge, Accuracy 2002: 5th International Conference on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Melbourne, Australia, July, 2002.
[19] Cracknell, A.P. Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing. 1998, 19(11):2025-2047.
[20] Davis, T.J. & Keller, C.P. Modelling and visualizing multiple spatial uncertainties. Computers & Geosciences, 1997, 23(4):397-408.
[21] DiBiase, D., MacEachren, A.M., Krygier, J.B. & Reeves, C. Animation and the role of Map Design. Cartography and Geographic Information Systems, 1992, 19(4):201-214.
[22] Djurcilov, S., Kim, K., Lermusiaux, P. & Pang, A.T. Volume rendering data with uncertainty information, In "Data visualization", Joint Eurographics - IEEE TCVG Symposium on Visualization, D. Ebert, J. M. Favre and R. Peikert (Eds.), Springer Verlag, 2001, pp.243-252, 355-356.
[23] Djurcilov, S., Kim, K., Lermusiaux, P. & Pang, A.T. Visualizing scalar volumetric data with uncertainty, Computers & Graphics, 2002, 26(2):239-248.
[24] Dykes, J.A. Exploring spatial data representation with dynamic graphics. Computers a Geosciences, 1997, 23(4):345-370.
[25] Ehlschlaeger, C.R., Shortridge, A.M. & Goodchild, M.F. Visualizing spatial data uncertainty using animation. Computers & Geosciences, 1997, 23(4):387-395.
[26] Evans, B.J. Dynamic display of spatial data-reliability: does it benefit the map user? Computers & Geosciences, 1997, 23(4):409-422.
[27] Fisher, P.F. Randomization and sound for the visualization of uncertain spatial information, In Visualization in Geographic Information Systems, ed. D. Unwin and H. Hearnshaw, pp.181-185. London: John Wiley & Sons, 1994a.
[28] Fisher, P.F. Hearing the reliability in classified remotely sensed images. Cartography and Geographic Information Systems, 1994b, 21(1):31-36.
[29] Fisher, P.F. Visualization of the reliability in classified remotely sensed images. Photogrammetric Engineering & Remote Sensing, 1994c, 60(7):905-910.
[30] Fisher, P.F. The pixel: a snare and a delusion. International Journal of Remote Sensing. 1997, 18(3):679-685.
[31] Foody, G.M. Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. International Journal of Remote Sensing. 1996a, 17(7): 1317-1340.
[32] Foody, G.M. Sharpening fuzzy classification output to refine the representation of sub-pixel land
cover distribution. International Journal of Remote Sensing, 1998, 19(13):2593-2599.
[33] Gahegan, M., Ehlers, M. A Framework for the modeling of Uncertainty between Remote Sensing and Geographic Information Systems, ISPRS Journal of Photogrammetry & Remote Sensing, 2000, 55:176-188.
[34] Ge, Y., Wang, J.F., Lou, X.G. SAR Imaging System Error Analysis Based on Mechanism Analysis Method, Proceeding 9~(th) International Symposium on Spatial Data Handling, 2000. Section 2b, p. 18.
[35] Geman, S. & Geman, D. Stochastic relaxation, gibbs distribution and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6):721-741.
[36] Goodchild, M.F. Communicating the Results of Accuracy Assessment: Metadata, Digital Libraries, and Assessing Fitness for use. H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in Natural Resources. 1999, p. 14.
[37] Goodchild, M.F. Models for uncertainty in area-class maps. Proceedings of the 2nd International Symposium on Spatial Data Quality '03, 19th-20th, March 2003, Hong Kong. (Eds. Shi, W.Z., Goodchild, M.F., Fisher, P.) pp. 1-9.
[38] Goodchild, M.F., Haining, R., Wise, S., et al. Integrating GIS and Spatial Data Analysis: Problems and Possibilities. International Journal of Geographical Information Systems, 1992, 6(5):407-423.
[39] Goodchild, M.F., Buttenfield, B. & Wood. J. On introduction to visualizing data validity. In H. Hearnshaw & D. Unwin, editors, Visualization in Geographical Information Systems, 1994, pp. 141-149. Wiley.
[40] Goodchild, M.F. & Jeansoulin, R. (editors) Data Quality in Geographic Information, from Error to Uncertainty (Paris: Hermes). 1998.
[41] Goodchild, M.F., Shortridge, A.M. & Fohl, P. Encapsulating simulation models with geospatial data sets, in Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, K. Lowell and A. Jaton (Editors). Ann Arbor Press, Michigan, 1999.
[42] Hand, D.J., Mannila, H. & Smyth, P. Principles of Data Mining. MIT Press, 2001.
[43] Hawking, S.W. A Brief History of Time: From the Big Bang to Black Holes. Bantam Dell Pub Group, 1993.
[44] Heisenberg, W. Uber den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik.Z. fur Phys. 1927,43:172-198.
[45] Hu, X.H., Lin, T.Y. & Han, J.C. A new rough sets model based on database systems. Fundamenta Informaticae, 2004, 59(2-3):135-152.
[46] Hunter, G.J. & Goodchild, M.F. Dealing with error in spatial databases: a simple case study. Photogrammetric Engineering & Remote Sensing, 1995, 61(5):529-537.
[47] Idrisi. Guide to GIS and image processing, Vol.2. 2001.
[48] Inselberg, A. The Plane with Parallel Coordinates, Special Issue on Computational Geometry of The Visual Computer 1985, 1:69-97.
[49] Isolde, S., Giorgos, M., Peggy, A. Visualization of Image Quality in Distributed Spatial Databases, in XXth ISPRS Congress, 12-23 July 2004, Istanbul, Turkey.
[50] Joseph, J. & Pfeiffer, Jr. Using brightness and saturation to visualize belief and uncertainty. Lecture Notes in Computer Science, 2317: 279-289. In Mary Hegarty, Bernd Meyer, and N. Hari Narayanan, editors, Diagrammatic Representation and Inference, Second International Conference, April 2002, Springer-Verlag, London, UK.
[51] Kardos, J. D., Moore, A. & Benwell, G. Visualising uncertainty in spatially-referenced attribute data using hierarchical data structures, Proceedings of GeoComputation 2003, University of Southampton, UK. 2003.
[52] Komorowski, J., Pawlak, Z., Polkowski, L. & Skowron, A. Rough sets: a tutorial, in Rough Fuzzy Hybridization: A New Trend in Decision-Making, edited by S. K. Pal and A. Skowron, Singapore: Springer-Verlag, 1999, pp. 3-98.
[53] Kraak, M. J. & Kousoulakou, A. A visualization environment for the space-time-cube. In: Fisher, P. F. (Ed), Developments in spatial data handling 11th International Symposium on Spatial Data Handling. Berlin, 23rd-25th August 2004. Springer Verlag, 2005, pp. 189-200.
[54] Kraak, M. J. & MacEachren, A. M. (Eds.) Special issue: visualization for exploration of spatial data. International Journal of Geographical Information Science, 1999, 13(4): 285-443.
[55] Kreuseler, M. Visualization of geographically related multidimensional data in virtual 3D scenes, Computers & Geoences, 2000, 26: 101-108.
[56] Krygier, J. Sound and Cartographic Visualization. In: D. Taylor & A. MacEachren, eds. Visualization in Modern Cartography. Oxford: Pergamon Press, 1994.
[57] Leung, Y, & Li, D. Y. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences, 2003, 153: 85-106.
[58] Liang, J. Y. & Shi, Z. Z. The information entropy, rough entropy and knowledge granulation in rough set theory, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(1): 37-46.
[59] Lillesand, T. M. & Kiefer, R. W. Remote sensing and image interpretation, Fourth Edition,2000.遥感与图像解译(第四版).彭望碌 徐先川 周涛 李小英 等译.北京:电子工业出版社,2003.
[60] Lodha, S. K., Sheehan, B., Alex T. P, & Wittenbrink, C. Visualizing geometric uncertainty of surface interpolants, Graphics Interface '96, Toronto, Canada, May 1996a, pp. 238-245.
[61] Lodha, S. K., Wilson, C. M, & Sheehan, R. E. LISTEN: Sounding uncertainty visualization, 7th IEEE Visualization '96 Conference, October 27-November 01, 1996b. San Francisco, California. p. 189.
[62] Longly, P. A., Goodchild, M. F., Maguire, D. J. & Rhind, D. W. Geographical information systems, Volume 1, Principles and technical issues, Second Edition. 1999.地理信息系统(上卷)原理与技术(第二版).唐中实 黄俊峰 尹平 等译.北京:电子工业出版社,2004.
[63] Lu, D., Moran, E. F., Batisteila, M. Linear mixture model applied to Amazonian vegetation classification, Remote sensing of environment, 2003, 87: 456-469.
[64] Lucieer, A. Uncertainties in Segmentation and their Visualisation, PhD dissertation. International Institute for Geo-lnformation Science and Earth Observation, Enschede, Netherlands. 2004. http://www.parbat.net/
[65] Lucieer, A., Kraak, M. J. Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty, International Journal of Geographical Information Science, 2004a, 18(5):491-512.
[66] MacEachren, A.M. & DiBiase, D.W. Animated maps of aggregate data: Conceptual and practical problems. Cartography and Geographic Information Systems, 1991, 18(4): 221-229.
[67] MacEachren, A.M. & Kraak, M.J. (Eds.) Special issue: exploratory cartographic visualization. Computers & Geosciences, 1997, 23(4):335-491.
[68] MacEachren, A.M. & Taylor, D.R.F. (Eds.) Visualization in Modern Cartography. Pergamon Press, Oxford, 1994. 345pp.
[69] Maguire, D.J. A research plan for GIS in the 1990s. In: Peter, J.S. and Moore, R.V., The association for geographic information yearbook. New York: Taylor and Francis, 1989.
[70] Marschallinger, R. Three-dimensional reconstruction and visualization of geological materials with IDL- examples and source code, Computers & Geosciences, 2001, 27:419-426.
[71] Maselli, F., Conese, C, & Petkov, L. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications, ISPRS Journal of Photogrammetry and Remote Sensing, 1994,49(2): 13-20.
[72] Mertens, K.C., Verbeke, L.P.C. & De Wulf, R.R. Sub-pixel mapping: a comparison of techniques. 25th EARSeL Symposium, Global Developments in Environmental Earth Observation from Space. Porto, Portugal, 6-11 June 2005. http://www.isn-oldenburg.de/projects/earseI-abstracts2005/ABS_Mertens.html
[73] Mertens, K.C., Verbeke, L.P.C, Ducheyne, E.I. & De Wulf, R.R. Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing, 2003, 24(21):4241-4247.
[74] Mertens, K.C., Verbeke, L.P.C, Westra, T. & De Wulf, R.R. Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment, 2004,91:225-236.
[75] Michele, C, Jose, A.M.R. & Bruno, C. Uncertainty propagation in models driven by remotely sensed data. Remote Sensing of Environment, 2001, 76:373-385.
[76] NexpRI. Geographical Information System for landscape analysis research programme. University of Utrecht, 1989.
[77] Newman, T.S. & Lee, W. On Visualizing uncertainty in volumetric data: techniques and their evaluation, Journal of Visual Language and Computing, 2004, 15:463-491.
[78] 0hrn, A., Komorowski, J. ROSETTA: A Rough Set Toolkit for Analysis of Data, Proc. Third International Joint Conference on Information Sciences, Fifth International Workshop on Rough Sets and Soft Computing (RSSC97), Durham, NC, USA, March 1-5, 1997, Vol.3, pp.403-407.
[79] Φhm, A. Discernibility and Rough Sets in Medicine: Tools and Applications, PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. NTNU report 1999:133, IDI report 1999:14.
[80] Openshaw, S. Learning to live with errors in spatial databases. In: Goodchild, M.F. & Gopal, S. (ed.), Accuracy of spatial databases. New York: Taylor and Francis, 1989, pp.263-276.
[81] Pang, A.T. Visualizing uncertainty in geo-spatial data, Proceedings of the Workshop on the Intersections between Oeospatial Information and Information Technology, National Academies committee of the Computer Science and Telecommunications Board, October 2001.
[82] Pang, A. T., Wittenbrink, C. M. & Lodha, S. K. Approaches to uncertainty visualization, The Visual Computer, 1997, 13(8): 370-390.
[83] Pawlak, Z. Rough sets. International Journal of Computer and Information Sciences, 1982, 11: 341-356.
[84] Rhodes, P. J., Laramee, R. S., Beregeron, D. R. & Sparr, T. M. Uncertainty visualization methods in isosurface rendering, EUROGRAPHICS 2003 Short Papers, pp. 83-88, September 1-5, 2003, Granada, Spain.
[85] Ruiz, C. P. & Lopez, F. J. A. Restoring SPOT images using PSF-derived deconvolution filters. International Journal of Remote Sensing. 2002, 23(12): 2379-2391.
[86] Shannon, C. E. A mathematical theory of communication, Bell System Technical Journal, July and October, 1948, 27: 379-423 & 623-656.
[87] Shi, W. Z. Modelling positional and thematic uncertainties in integration of remote sensing and GIS, ITC Publication, Number 22, 1994.
[88] Shneiderman, B. Designing the User Interface: Strategies for Effective Human-Computer Interaction. 4th ed. Boston, MA: Addison-Wesley, 2004.
[89] Tatem, A. J., Lewis, H. G, Atkinson, P. M. & Nixon, M. S. Land cover simulation and prediction at the sub-pixel scale using a Hopfield neural network. Proceedings, 26th Annual Conference and Exhibition of the Remote Sensing Society, Leicester, UK;12-14 September 2000. The Remote Sensing Society, Nottingham.
[90] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing. 2001a, 39(4): 781-796.
[91] Tatem, A. J., Lewis, H. G., Atkinson, P. M. and Nixon, M. S. Multiple class land cover mapping at the sub-pixel scale using a Hopfield neural network. Journal of Applied Earth Observation and Geoinformation. 2001b, 3(2): 184-190.
[92] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment, 2002, 79(1): 1-14.
[93] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network. International Journal of Geographical Information Science, 2003, 17(7): 647-672.
[94] Theodoridis, S. & Koutroumbas, K. Pattern Recognition, 2nd Edition. Elsevier Science, 2003.北京:机械工业出版社,2003.
[95] Thomson, J., Hetzler, B., MacEachren, A., Gahegan, M. & Pavel, M. A typology for visualizing uncertainty, in Conference on Visualization and Data Analysis 2005 (part of the IS & T/SPIE Symposium on Electronic Imaging 2005), 16-20 January 2005, San Jose, CA USA.
[96] Thrower, N. J. W. Animated cartography in the United States. International Yearbook of Cartography, 1961, 1: 20-30.
[97] Tobler, W. R. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46: 234-240.
[98] van der Wel, F. J. M., van der Gaag, L. C. & Gorte, B. G. H. Visual exploration of uncertainty in remote sensing classification, Computers & Geosciences, 1998, 24(4): 335-343.
[99] van der Wel, F. J. M., Hootsmans, R. M. & Ormeling, F. Visualization of data quality, In: McEachren, A. M., Taylor, D. R. F. (Eds.), Visualization in Modern Cartography. Pergamon Press, Oxford, 1994. pp. 313-331.
[100] Verhoeye, J. & De Wulf, R. R. Sub-pixel mapping of Sahelian wetlands using multi-temporal SPOT-VEGETATION images. In: VEGETATION 2000. Lake Maggiore, Italy, April 3-6, 2000. http://vegetation.cnes.fr/vgtprep/vgt2000/verhoye.pdf
[101] Verhoeye, J. & De Wulf, R. R. Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment, 2002, 79: 96-104.
[102] Vieira, C. A. O., Mather, P. M. & Aplin, P. Assessing the Positional and Thematic Accuracy of Remotely Sensed Data. in ⅩⅩth ISPRS Congress, 12-23 July 2004, Istanbul, Turkey.
[103] Webster, R. & Beckeft, P. H. T. Quality and usefulness of soil maps. Nature, 1968, 219: 680-682.
[104] Zadah, A. Fuzzy sets. Information and control, 1965, 8: 338-353.
[105] Zhang, J. X. & Goodehild, M. F. Uncertainty in geographical information, Taylor & Francis, 2002.
[106] 柏延臣.遥感信息提取的不确定性和尺度效应研究.中国科学院地理科学与资源研究所博士论文,2002.
[107] 柏延臣,王劲峰.遥感专题分类不确定性评价研究:进展、问题与展望.地球科学进展,2005,20(11):1218-1225.
[108] 承继成,郭华东,史文中.遥感数据的不确定性问题.北京:科学出版社,2004.
[109] 戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析.北京:清华大学出版社,2004.
[110] 邓聚龙.灰理论基础.武汉:华中科技大学出版社,2002.
[111] 葛咏.计算机模拟SAR集成框架研究.系统仿真学报,2001a,13(2):201-205.
[112] 葛咏.机载合成孔径雷达(SAR)不确定性分析——理论、系统及应用.中国科学院地理科学与资源研究所博士论文,2001b.
[113] 葛咏.遥感信息不确定性研究—理论及应用.中国科学院博士后研究工作报告.中国科学院遥感应用研究所(北京),2003.
[114] 葛咏,王劲峰,梁怡,王贞松.相位误差、位置误差和灰度误差三者关系及SAR原始图像像元不确定性.遥感学报,2003a,7(4):285-291.
[115] 葛咏,王劲峰.遥感信息的不确定性研究误差传递模型.北京:地质出版社,2003b.
[116] 葛咏,王劲峰,梁怡,马江洪.遥感信息的不确定性研究.遥感学报,2004,8(4):339-348.
[117] 何全军,程彬,许惠平.基于IDL的地形三维可视化实现.世界地质,2004,23(1):85-89.
[118] 黄幼才,刘文宝,李宗华,肖道纲.GIS空间数据误差分析和处理.武汉:中国地质大学出版社,1994.
[119] 吉长东.基于IDL的城市小区景观VGIS平台研究与实现.中国优秀博硕士学位论文全文数据库网,2002.http:///www.cdmd.cnki.net http://www.cngis.net/paper.htm
[120] 梁吉业,李德玉.信息系统中的不确定性与知识获取.北京:科学出版社,2005.
[121] 凌峰,张秋文,王乘,周建中.基于元胞自动机模型的遥感图像亚像元定位.中国图象图 形学报,2005,10(7):916-921.
[122] 刘勘,周晓峥,周洞汝.基于平行坐标法的可视数据挖掘.计算机工程与应用,2003,193-195.
[123] 刘文宝,GIS空间数据的不确定性理论,博士论文.武汉测绘科技大学,1995.
[124] 吕长春,王忠武,钱少猛.混合像元分解模型综述.遥感信息,2003,(3):55-58.
[125] 钱学森,于景元,戴汝为.一个科学新领域——开放的复杂巨系统及其方法论.自然杂志.1990,13(1):5-12,66.
[126] 史文中.空间数据与空间分析不确定性原理.北京:科学出版社,2005.
[127] 孙家[扌丙].遥感原理与应用.武汉:武汉大学出版社,2003.
[128] 孙家广,杨长贵.计算机图形学(第三版).北京:清华大学出版社,2000.pp.553-556.
[129] 吴健平,杨星卫.遥感数据分类结果的精度分析.遥感技术与应用,1995.10(1):17-24.
[130] 邬伦,于海龙,高振纪等.GIS不确定性框架体系与数据不确定性研究方法.地理学与国土研究,2002,18(4):1-5.
[131] 张景雄,杜道生.位置不确定性与属性不确定性的场模型.测绘学报,1999,28(3):244-249.
[132] 张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.北京:科学出版社,2001.
[133] 周晓峥,刘勘,孟波.多维数据集的平行坐标表示及聚簇分析.计算机工程,2002,28(1):94-95,143.
[2] Agumya, A. & Hunter, G. J. Responding to the consequences of uncertainty in geographical data, International Journal of Geographical Information Science, 2002, 16(5): 405-417.
[3] Aplin, P. & Atkinson, P. M. Sub-pixel land cover mapping for per-field classification. International Journal of Remote Sensing, 2001, 22(14): 2853-2858.
[4] Aplin, P., Atkinson, P. M., Curran, P. J. Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment, 1999, 68: 206-216.
[5] Appleton, K. Representing uncertainty in geovisualisation: a research agenda. European Science Foundation EURESCO conference "Geovisualisation", in Kolymbari, Crete, 13th-18th March 2004.
[6] Atkinson, P. M. Mapping sub-pixel vector boundaries from remotely sensed images. In Proceedings of GISRUK '96. University of Kent, Canterbury, UK, 29-41. In: Kemp Z. Edi: Innovations in GIS 4, London: Taylor and Francis, 1997, pp. 166-180.
[7] Atkinson, P. M. Super-resolution target mapping from soft classified remotely sensed imagery. In: Proceedings of the 5th International Conference on GeoComputation. Leeds, UK, University of Leeds. CD-ROM. 2001. http://www.geocomputation.org/2001/papers/atkinson.pdf
[8] Bastin, L. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixeis. International Journal of Remote Sensing. 1997, 18(17): 3629-3648.
[9] Bastin, L., Wood, J. & Fisher, P. F. Visualization of fuzzy spatial information in spatial decision-making. In: Lowell, K., Jaton, A. (Eds.), Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources. Ann Arbor Press, Chelsea, MI, 1999a. pp. 151-156.
[10] Bastin, L., Wood, J. & Fisher, P.F. Visualizing uncertainty in fuzzy thematic classi.cations of multispectral satellite imagery. In: Shi, W., Goodchild, M. F., Fisher, P. F. (Eds.), Proceedings of the International Symposium on Spatial Data Quality'99. Polytechnic University, Hong Kong, 1999b. pp. 243-252.
[11] Bastin, L., Fisher, P. F., & Wood, J. Visualizing uncertainty in multi-spectral remotely sensed imagery, Computers & Geosciences, 2002, 28(3): 337-350.
[12] Blakney, W. G. G. Accuracy standards for topographic mapping. Photogrammetric Engineering and Remote Sensing, 1968, 34(10): 1040-1042.
[13] Burrough, P. A. & McDonnell, R. A. Principles of geographical information systems. Oxford University Press, 1998.
[14] Carlo, W. D. Exploring multi-dimensional remote sensing data with a virtual reality system, Geographical & Environmental Modelling, 2000, 4(1):7-20.
[15] Carmel, Y., Dean, D.J., Flather, C.H. Combining location and classification error sources for estimating multi-temporal database accuracy, PE&RS, 2001, 67(7):865-872.
[16] Chen, P.C. Developing a personal computer-based data visualization system using public domain software, SPIE's Electronic Imaging '99, January 27,1999, San Jose, California, USA.
[17] Congalton, R.G. & Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles of Practices. Lewis Publishers, 1999.
[18] Couclelis, H. Living with uncertainty: GIS and the limits of geographic knowledge, Accuracy 2002: 5th International Conference on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Melbourne, Australia, July, 2002.
[19] Cracknell, A.P. Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing. 1998, 19(11):2025-2047.
[20] Davis, T.J. & Keller, C.P. Modelling and visualizing multiple spatial uncertainties. Computers & Geosciences, 1997, 23(4):397-408.
[21] DiBiase, D., MacEachren, A.M., Krygier, J.B. & Reeves, C. Animation and the role of Map Design. Cartography and Geographic Information Systems, 1992, 19(4):201-214.
[22] Djurcilov, S., Kim, K., Lermusiaux, P. & Pang, A.T. Volume rendering data with uncertainty information, In "Data visualization", Joint Eurographics - IEEE TCVG Symposium on Visualization, D. Ebert, J. M. Favre and R. Peikert (Eds.), Springer Verlag, 2001, pp.243-252, 355-356.
[23] Djurcilov, S., Kim, K., Lermusiaux, P. & Pang, A.T. Visualizing scalar volumetric data with uncertainty, Computers & Graphics, 2002, 26(2):239-248.
[24] Dykes, J.A. Exploring spatial data representation with dynamic graphics. Computers a Geosciences, 1997, 23(4):345-370.
[25] Ehlschlaeger, C.R., Shortridge, A.M. & Goodchild, M.F. Visualizing spatial data uncertainty using animation. Computers & Geosciences, 1997, 23(4):387-395.
[26] Evans, B.J. Dynamic display of spatial data-reliability: does it benefit the map user? Computers & Geosciences, 1997, 23(4):409-422.
[27] Fisher, P.F. Randomization and sound for the visualization of uncertain spatial information, In Visualization in Geographic Information Systems, ed. D. Unwin and H. Hearnshaw, pp.181-185. London: John Wiley & Sons, 1994a.
[28] Fisher, P.F. Hearing the reliability in classified remotely sensed images. Cartography and Geographic Information Systems, 1994b, 21(1):31-36.
[29] Fisher, P.F. Visualization of the reliability in classified remotely sensed images. Photogrammetric Engineering & Remote Sensing, 1994c, 60(7):905-910.
[30] Fisher, P.F. The pixel: a snare and a delusion. International Journal of Remote Sensing. 1997, 18(3):679-685.
[31] Foody, G.M. Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. International Journal of Remote Sensing. 1996a, 17(7): 1317-1340.
[32] Foody, G.M. Sharpening fuzzy classification output to refine the representation of sub-pixel land
cover distribution. International Journal of Remote Sensing, 1998, 19(13):2593-2599.
[33] Gahegan, M., Ehlers, M. A Framework for the modeling of Uncertainty between Remote Sensing and Geographic Information Systems, ISPRS Journal of Photogrammetry & Remote Sensing, 2000, 55:176-188.
[34] Ge, Y., Wang, J.F., Lou, X.G. SAR Imaging System Error Analysis Based on Mechanism Analysis Method, Proceeding 9~(th) International Symposium on Spatial Data Handling, 2000. Section 2b, p. 18.
[35] Geman, S. & Geman, D. Stochastic relaxation, gibbs distribution and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6):721-741.
[36] Goodchild, M.F. Communicating the Results of Accuracy Assessment: Metadata, Digital Libraries, and Assessing Fitness for use. H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in Natural Resources. 1999, p. 14.
[37] Goodchild, M.F. Models for uncertainty in area-class maps. Proceedings of the 2nd International Symposium on Spatial Data Quality '03, 19th-20th, March 2003, Hong Kong. (Eds. Shi, W.Z., Goodchild, M.F., Fisher, P.) pp. 1-9.
[38] Goodchild, M.F., Haining, R., Wise, S., et al. Integrating GIS and Spatial Data Analysis: Problems and Possibilities. International Journal of Geographical Information Systems, 1992, 6(5):407-423.
[39] Goodchild, M.F., Buttenfield, B. & Wood. J. On introduction to visualizing data validity. In H. Hearnshaw & D. Unwin, editors, Visualization in Geographical Information Systems, 1994, pp. 141-149. Wiley.
[40] Goodchild, M.F. & Jeansoulin, R. (editors) Data Quality in Geographic Information, from Error to Uncertainty (Paris: Hermes). 1998.
[41] Goodchild, M.F., Shortridge, A.M. & Fohl, P. Encapsulating simulation models with geospatial data sets, in Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, K. Lowell and A. Jaton (Editors). Ann Arbor Press, Michigan, 1999.
[42] Hand, D.J., Mannila, H. & Smyth, P. Principles of Data Mining. MIT Press, 2001.
[43] Hawking, S.W. A Brief History of Time: From the Big Bang to Black Holes. Bantam Dell Pub Group, 1993.
[44] Heisenberg, W. Uber den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik.Z. fur Phys. 1927,43:172-198.
[45] Hu, X.H., Lin, T.Y. & Han, J.C. A new rough sets model based on database systems. Fundamenta Informaticae, 2004, 59(2-3):135-152.
[46] Hunter, G.J. & Goodchild, M.F. Dealing with error in spatial databases: a simple case study. Photogrammetric Engineering & Remote Sensing, 1995, 61(5):529-537.
[47] Idrisi. Guide to GIS and image processing, Vol.2. 2001.
[48] Inselberg, A. The Plane with Parallel Coordinates, Special Issue on Computational Geometry of The Visual Computer 1985, 1:69-97.
[49] Isolde, S., Giorgos, M., Peggy, A. Visualization of Image Quality in Distributed Spatial Databases, in XXth ISPRS Congress, 12-23 July 2004, Istanbul, Turkey.
[50] Joseph, J. & Pfeiffer, Jr. Using brightness and saturation to visualize belief and uncertainty. Lecture Notes in Computer Science, 2317: 279-289. In Mary Hegarty, Bernd Meyer, and N. Hari Narayanan, editors, Diagrammatic Representation and Inference, Second International Conference, April 2002, Springer-Verlag, London, UK.
[51] Kardos, J. D., Moore, A. & Benwell, G. Visualising uncertainty in spatially-referenced attribute data using hierarchical data structures, Proceedings of GeoComputation 2003, University of Southampton, UK. 2003.
[52] Komorowski, J., Pawlak, Z., Polkowski, L. & Skowron, A. Rough sets: a tutorial, in Rough Fuzzy Hybridization: A New Trend in Decision-Making, edited by S. K. Pal and A. Skowron, Singapore: Springer-Verlag, 1999, pp. 3-98.
[53] Kraak, M. J. & Kousoulakou, A. A visualization environment for the space-time-cube. In: Fisher, P. F. (Ed), Developments in spatial data handling 11th International Symposium on Spatial Data Handling. Berlin, 23rd-25th August 2004. Springer Verlag, 2005, pp. 189-200.
[54] Kraak, M. J. & MacEachren, A. M. (Eds.) Special issue: visualization for exploration of spatial data. International Journal of Geographical Information Science, 1999, 13(4): 285-443.
[55] Kreuseler, M. Visualization of geographically related multidimensional data in virtual 3D scenes, Computers & Geoences, 2000, 26: 101-108.
[56] Krygier, J. Sound and Cartographic Visualization. In: D. Taylor & A. MacEachren, eds. Visualization in Modern Cartography. Oxford: Pergamon Press, 1994.
[57] Leung, Y, & Li, D. Y. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences, 2003, 153: 85-106.
[58] Liang, J. Y. & Shi, Z. Z. The information entropy, rough entropy and knowledge granulation in rough set theory, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(1): 37-46.
[59] Lillesand, T. M. & Kiefer, R. W. Remote sensing and image interpretation, Fourth Edition,2000.遥感与图像解译(第四版).彭望碌 徐先川 周涛 李小英 等译.北京:电子工业出版社,2003.
[60] Lodha, S. K., Sheehan, B., Alex T. P, & Wittenbrink, C. Visualizing geometric uncertainty of surface interpolants, Graphics Interface '96, Toronto, Canada, May 1996a, pp. 238-245.
[61] Lodha, S. K., Wilson, C. M, & Sheehan, R. E. LISTEN: Sounding uncertainty visualization, 7th IEEE Visualization '96 Conference, October 27-November 01, 1996b. San Francisco, California. p. 189.
[62] Longly, P. A., Goodchild, M. F., Maguire, D. J. & Rhind, D. W. Geographical information systems, Volume 1, Principles and technical issues, Second Edition. 1999.地理信息系统(上卷)原理与技术(第二版).唐中实 黄俊峰 尹平 等译.北京:电子工业出版社,2004.
[63] Lu, D., Moran, E. F., Batisteila, M. Linear mixture model applied to Amazonian vegetation classification, Remote sensing of environment, 2003, 87: 456-469.
[64] Lucieer, A. Uncertainties in Segmentation and their Visualisation, PhD dissertation. International Institute for Geo-lnformation Science and Earth Observation, Enschede, Netherlands. 2004. http://www.parbat.net/
[65] Lucieer, A., Kraak, M. J. Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty, International Journal of Geographical Information Science, 2004a, 18(5):491-512.
[66] MacEachren, A.M. & DiBiase, D.W. Animated maps of aggregate data: Conceptual and practical problems. Cartography and Geographic Information Systems, 1991, 18(4): 221-229.
[67] MacEachren, A.M. & Kraak, M.J. (Eds.) Special issue: exploratory cartographic visualization. Computers & Geosciences, 1997, 23(4):335-491.
[68] MacEachren, A.M. & Taylor, D.R.F. (Eds.) Visualization in Modern Cartography. Pergamon Press, Oxford, 1994. 345pp.
[69] Maguire, D.J. A research plan for GIS in the 1990s. In: Peter, J.S. and Moore, R.V., The association for geographic information yearbook. New York: Taylor and Francis, 1989.
[70] Marschallinger, R. Three-dimensional reconstruction and visualization of geological materials with IDL- examples and source code, Computers & Geosciences, 2001, 27:419-426.
[71] Maselli, F., Conese, C, & Petkov, L. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications, ISPRS Journal of Photogrammetry and Remote Sensing, 1994,49(2): 13-20.
[72] Mertens, K.C., Verbeke, L.P.C. & De Wulf, R.R. Sub-pixel mapping: a comparison of techniques. 25th EARSeL Symposium, Global Developments in Environmental Earth Observation from Space. Porto, Portugal, 6-11 June 2005. http://www.isn-oldenburg.de/projects/earseI-abstracts2005/ABS_Mertens.html
[73] Mertens, K.C., Verbeke, L.P.C, Ducheyne, E.I. & De Wulf, R.R. Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing, 2003, 24(21):4241-4247.
[74] Mertens, K.C., Verbeke, L.P.C, Westra, T. & De Wulf, R.R. Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment, 2004,91:225-236.
[75] Michele, C, Jose, A.M.R. & Bruno, C. Uncertainty propagation in models driven by remotely sensed data. Remote Sensing of Environment, 2001, 76:373-385.
[76] NexpRI. Geographical Information System for landscape analysis research programme. University of Utrecht, 1989.
[77] Newman, T.S. & Lee, W. On Visualizing uncertainty in volumetric data: techniques and their evaluation, Journal of Visual Language and Computing, 2004, 15:463-491.
[78] 0hrn, A., Komorowski, J. ROSETTA: A Rough Set Toolkit for Analysis of Data, Proc. Third International Joint Conference on Information Sciences, Fifth International Workshop on Rough Sets and Soft Computing (RSSC97), Durham, NC, USA, March 1-5, 1997, Vol.3, pp.403-407.
[79] Φhm, A. Discernibility and Rough Sets in Medicine: Tools and Applications, PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. NTNU report 1999:133, IDI report 1999:14.
[80] Openshaw, S. Learning to live with errors in spatial databases. In: Goodchild, M.F. & Gopal, S. (ed.), Accuracy of spatial databases. New York: Taylor and Francis, 1989, pp.263-276.
[81] Pang, A.T. Visualizing uncertainty in geo-spatial data, Proceedings of the Workshop on the Intersections between Oeospatial Information and Information Technology, National Academies committee of the Computer Science and Telecommunications Board, October 2001.
[82] Pang, A. T., Wittenbrink, C. M. & Lodha, S. K. Approaches to uncertainty visualization, The Visual Computer, 1997, 13(8): 370-390.
[83] Pawlak, Z. Rough sets. International Journal of Computer and Information Sciences, 1982, 11: 341-356.
[84] Rhodes, P. J., Laramee, R. S., Beregeron, D. R. & Sparr, T. M. Uncertainty visualization methods in isosurface rendering, EUROGRAPHICS 2003 Short Papers, pp. 83-88, September 1-5, 2003, Granada, Spain.
[85] Ruiz, C. P. & Lopez, F. J. A. Restoring SPOT images using PSF-derived deconvolution filters. International Journal of Remote Sensing. 2002, 23(12): 2379-2391.
[86] Shannon, C. E. A mathematical theory of communication, Bell System Technical Journal, July and October, 1948, 27: 379-423 & 623-656.
[87] Shi, W. Z. Modelling positional and thematic uncertainties in integration of remote sensing and GIS, ITC Publication, Number 22, 1994.
[88] Shneiderman, B. Designing the User Interface: Strategies for Effective Human-Computer Interaction. 4th ed. Boston, MA: Addison-Wesley, 2004.
[89] Tatem, A. J., Lewis, H. G, Atkinson, P. M. & Nixon, M. S. Land cover simulation and prediction at the sub-pixel scale using a Hopfield neural network. Proceedings, 26th Annual Conference and Exhibition of the Remote Sensing Society, Leicester, UK;12-14 September 2000. The Remote Sensing Society, Nottingham.
[90] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing. 2001a, 39(4): 781-796.
[91] Tatem, A. J., Lewis, H. G., Atkinson, P. M. and Nixon, M. S. Multiple class land cover mapping at the sub-pixel scale using a Hopfield neural network. Journal of Applied Earth Observation and Geoinformation. 2001b, 3(2): 184-190.
[92] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment, 2002, 79(1): 1-14.
[93] Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S. Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network. International Journal of Geographical Information Science, 2003, 17(7): 647-672.
[94] Theodoridis, S. & Koutroumbas, K. Pattern Recognition, 2nd Edition. Elsevier Science, 2003.北京:机械工业出版社,2003.
[95] Thomson, J., Hetzler, B., MacEachren, A., Gahegan, M. & Pavel, M. A typology for visualizing uncertainty, in Conference on Visualization and Data Analysis 2005 (part of the IS & T/SPIE Symposium on Electronic Imaging 2005), 16-20 January 2005, San Jose, CA USA.
[96] Thrower, N. J. W. Animated cartography in the United States. International Yearbook of Cartography, 1961, 1: 20-30.
[97] Tobler, W. R. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46: 234-240.
[98] van der Wel, F. J. M., van der Gaag, L. C. & Gorte, B. G. H. Visual exploration of uncertainty in remote sensing classification, Computers & Geosciences, 1998, 24(4): 335-343.
[99] van der Wel, F. J. M., Hootsmans, R. M. & Ormeling, F. Visualization of data quality, In: McEachren, A. M., Taylor, D. R. F. (Eds.), Visualization in Modern Cartography. Pergamon Press, Oxford, 1994. pp. 313-331.
[100] Verhoeye, J. & De Wulf, R. R. Sub-pixel mapping of Sahelian wetlands using multi-temporal SPOT-VEGETATION images. In: VEGETATION 2000. Lake Maggiore, Italy, April 3-6, 2000. http://vegetation.cnes.fr/vgtprep/vgt2000/verhoye.pdf
[101] Verhoeye, J. & De Wulf, R. R. Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment, 2002, 79: 96-104.
[102] Vieira, C. A. O., Mather, P. M. & Aplin, P. Assessing the Positional and Thematic Accuracy of Remotely Sensed Data. in ⅩⅩth ISPRS Congress, 12-23 July 2004, Istanbul, Turkey.
[103] Webster, R. & Beckeft, P. H. T. Quality and usefulness of soil maps. Nature, 1968, 219: 680-682.
[104] Zadah, A. Fuzzy sets. Information and control, 1965, 8: 338-353.
[105] Zhang, J. X. & Goodehild, M. F. Uncertainty in geographical information, Taylor & Francis, 2002.
[106] 柏延臣.遥感信息提取的不确定性和尺度效应研究.中国科学院地理科学与资源研究所博士论文,2002.
[107] 柏延臣,王劲峰.遥感专题分类不确定性评价研究:进展、问题与展望.地球科学进展,2005,20(11):1218-1225.
[108] 承继成,郭华东,史文中.遥感数据的不确定性问题.北京:科学出版社,2004.
[109] 戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析.北京:清华大学出版社,2004.
[110] 邓聚龙.灰理论基础.武汉:华中科技大学出版社,2002.
[111] 葛咏.计算机模拟SAR集成框架研究.系统仿真学报,2001a,13(2):201-205.
[112] 葛咏.机载合成孔径雷达(SAR)不确定性分析——理论、系统及应用.中国科学院地理科学与资源研究所博士论文,2001b.
[113] 葛咏.遥感信息不确定性研究—理论及应用.中国科学院博士后研究工作报告.中国科学院遥感应用研究所(北京),2003.
[114] 葛咏,王劲峰,梁怡,王贞松.相位误差、位置误差和灰度误差三者关系及SAR原始图像像元不确定性.遥感学报,2003a,7(4):285-291.
[115] 葛咏,王劲峰.遥感信息的不确定性研究误差传递模型.北京:地质出版社,2003b.
[116] 葛咏,王劲峰,梁怡,马江洪.遥感信息的不确定性研究.遥感学报,2004,8(4):339-348.
[117] 何全军,程彬,许惠平.基于IDL的地形三维可视化实现.世界地质,2004,23(1):85-89.
[118] 黄幼才,刘文宝,李宗华,肖道纲.GIS空间数据误差分析和处理.武汉:中国地质大学出版社,1994.
[119] 吉长东.基于IDL的城市小区景观VGIS平台研究与实现.中国优秀博硕士学位论文全文数据库网,2002.http:///www.cdmd.cnki.net http://www.cngis.net/paper.htm
[120] 梁吉业,李德玉.信息系统中的不确定性与知识获取.北京:科学出版社,2005.
[121] 凌峰,张秋文,王乘,周建中.基于元胞自动机模型的遥感图像亚像元定位.中国图象图 形学报,2005,10(7):916-921.
[122] 刘勘,周晓峥,周洞汝.基于平行坐标法的可视数据挖掘.计算机工程与应用,2003,193-195.
[123] 刘文宝,GIS空间数据的不确定性理论,博士论文.武汉测绘科技大学,1995.
[124] 吕长春,王忠武,钱少猛.混合像元分解模型综述.遥感信息,2003,(3):55-58.
[125] 钱学森,于景元,戴汝为.一个科学新领域——开放的复杂巨系统及其方法论.自然杂志.1990,13(1):5-12,66.
[126] 史文中.空间数据与空间分析不确定性原理.北京:科学出版社,2005.
[127] 孙家[扌丙].遥感原理与应用.武汉:武汉大学出版社,2003.
[128] 孙家广,杨长贵.计算机图形学(第三版).北京:清华大学出版社,2000.pp.553-556.
[129] 吴健平,杨星卫.遥感数据分类结果的精度分析.遥感技术与应用,1995.10(1):17-24.
[130] 邬伦,于海龙,高振纪等.GIS不确定性框架体系与数据不确定性研究方法.地理学与国土研究,2002,18(4):1-5.
[131] 张景雄,杜道生.位置不确定性与属性不确定性的场模型.测绘学报,1999,28(3):244-249.
[132] 张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.北京:科学出版社,2001.
[133] 周晓峥,刘勘,孟波.多维数据集的平行坐标表示及聚簇分析.计算机工程,2002,28(1):94-95,143.