可见光遥感图像分割与提取研究
|
摘要
图像分割是遥感图像处理的重要研究内容。可见光遥感图像的处理在军事和民用方面都具有广泛的应用,而对可见光遥感图像的分割是其中的重要的研究内容之一。现有的遥感目标提取方法大都是在红外图像或者SAR图像上对某一类特定目标进行的,而可见光遥感图像上的分割方法大都是针对中低分辨率图像上的地物地貌分类。将中低分辨率遥感图像上的地物分类看作是一种分割,对这种区域性目标的现有分割方法通常效率比较低、实时性比较差。而对中高分辨率上边界清晰的地物目标而言,目前的提取方法基本处于人工经验判读或人机交互的半自动处理阶段,需要解决地物目标提取的智能化与自动化。本文针对可见光遥感图像中感兴趣目标分割提取的几个关键问题进行研究,根据不同的分割需求,对中低分辨率的区域性目标或中高分辨率的弱小目标分别采用不同的分割或提取方法。论文中对实际遥感图像进行了实验,验证了所提出方法的有效性。
本文在对已有的遥感图像分割算法进行充分研究与分析的基础上,所做的主要研究工作如下:
(1)考虑中低分辨率可见光遥感图像。对其上的区域性目标的分割问题进行了深入研究。针对可见光遥感图像上的城区目标通常具有边界模糊、连通面积大而导致分割速度慢等特点,本文基于模糊集理论提出一种矢量模糊分割法,通过样板法与模糊统计法相结合的方法构造模糊隶属函数,并构造了一个模糊训练过程来验证方法的有效性。
(2)研究了中高分辨率可见光遥感图像中感兴趣目标分割的自动化技术。现有的目标分割方法,要么是需要先验知识的有监督的自动分割,要么是无需先验知识但需要人工初始化的无监督分割。本文依据解决计算机视觉中的丢失数据问题的思路,对最大期望法的初始化方法进行改进,实现了迭代过程中参数的自动初始化,从而实现了感兴趣目标的自动分割。
(3)当感兴趣目标处在一个比较复杂的背景下,需要引入额外的附加先验才能够实现提取。论文引入了目标的形状先验。对复杂背景下的目标以受云层遮挡的海上舰船为例,通过分析获得舰船目标的形状模板作为先验,基于水平集理论的形状距离表示,将目标与复杂背景放在一起构造模型,并构造对应的能量函数,在能量最小化的过程中实现感兴趣目标的分割提取。
(4)最后,为了实现完整的可见光遥感图像上感兴趣目标的解译识别,在保证高准确率和低时间复杂度的同时,建立了一个感兴趣目标的人机交互分割提取系统。研究工作中的新贡献在于:
基于贝叶斯准则提出了城区的矢量模糊分割方法,相较于传统的多尺度分割方法,该方法快速有效,且具有较高的准确率。
对视觉上的参数估计提出了一种自动初始化方法,称之为方向标定法。使用该方法时,目标分割过程既无需初始化也无需人工参与,只依据图像自身的光谱属性和颜色属性通过自身的初始化与迭代来实现。实验验证了该方法的有效性和可靠性。
针对有云层遮挡的海上舰船提出了一种云层舰船模型(cloud cover ship model),将感兴趣目标与背景作为一个整体来看待。根据可见光遥感图像上云层是否存在阴影建立不同的模型以及对应的能量函数。实验验证了该方法的有效性。
综上,本文的研究工作首先从理论上进行分析,进而进行相应的算法设计,并通过实验验证了各个算法的有效性。
Segmentation is plays a vital role in remote sensing image processing. In the meanwhile, optical remote sensing image plays an important role in both military and civil situations. Therefore it is of great value about the research on optical remote sensing image segmentation. Existing remote objects extraction research mostly focuses on some kind of special objects in infrared images or SAR images. But the segmentation of optical remote sensing image mostly focuses on surface features landscape classification of low-middle spatial resolution optical remote sensing images. Taking the surface features landscape classification of low-middle spatial resolution optical remote sensing images as segmentation, existing methods mostly cannot get satisfied efficiency and reliability. For the surface objects with clear border and high-middle spatial resolution, the existing extraction methods are mostly depends on human experience or semi-automatic human-computer interaction. So the intelligent and automatic methods are necessary. In this paper, we study some key issues on objects segmentation and extraction in optical remote sensing images. Based on different segmentation requirements, for the low-middle spatial regional objects or high-middle small objects, different segmentation or extraction methods are proposed here. Experiment result on the actual remote sensing images gives evidence of this method’s efficiency.
In this dissertation, based on the existing segmentation research of the remote sensing image processing; our work are as follows:
Firstly, low-middle spatial resolution optical remote sensing image is taken into account. We take urban as an example of area objects and focus on the segmentation. The urban objects usually have a fuzzy boundary and Connectivity large area, so some segmentation speed is very slow. In this dissertation, a vectorized fuzzy segmentation method is proposed based on fuzzy set theory. The fuzzy membership function is constructed based on the model method and the fuzzy statistical method. The effectiveness of our method is verified by a fuzzy training process. Experiment result shows more efficient and higher accuracy compared with the traditional method.
Secondly, we consider the segmentation and extraction in middle-high resolution optical remote sensing images. The existing segmentation is either the supervised automatic segmentation with priori knowledge or the unsupervised segmentation with human initialization. A complete automization method is proposed, without initialization or human interaction. It is a data loss problem in computer vision. We do some work with the initialization of expectation maximization and propose a direction labeling method, which is used in the iterations of parameter estimization process. Experiment result shows the efficiency and reliability.
Thirdly, shape prior is introduced when the object of interest is in complicated background, such as ship objects under cloud in sea background. A cloud cover ship model is proposed based on features of optical remote sensing images. Based on the prior of the ship’s shape template, the model is constructed by putting the object and complex background together. Then the energy functions are constructed, and ship extraction is completed until the corresponding energy has been minimized. Experiment result gives evidence of this model’s efficiency.
Finally, in order to get an object interpretation in the whole optical remote sensing image, we propose a human interactive interpretation system for objects of interest, which assures high accuracy and low time complexity at the same time. Our contributions are as follows:
A vectorization fuzzy segmentation method for urban area is proposed based on the Bayes Rule. Compared with traditional multi-scale segmentation, our method is more effective and more accurate.
A direction labeling method with automatic initialization is proposed for the parameter estimation of the vision. The object segmentation process is realized with its own initialization and iteration. The process only needs the spectrum and color attributes of the image, without initialization and human interaction. Experiment result shows the efficiency and reliability.
A cloud over ship model is proposed for the ship objects under cloud in sea background. And the model is constructed by putting the object and complex background together. Different energy functions are constructed according to whether there is a cloud shadow. Experiment result shows the efficiency.
In this dissertation, some research on theory analysis is presented at the beginning, and then some corresponding algorithms are constructed. Experiment result shows the efficiency and reliability of these algorithms.
本文在对已有的遥感图像分割算法进行充分研究与分析的基础上,所做的主要研究工作如下:
(1)考虑中低分辨率可见光遥感图像。对其上的区域性目标的分割问题进行了深入研究。针对可见光遥感图像上的城区目标通常具有边界模糊、连通面积大而导致分割速度慢等特点,本文基于模糊集理论提出一种矢量模糊分割法,通过样板法与模糊统计法相结合的方法构造模糊隶属函数,并构造了一个模糊训练过程来验证方法的有效性。
(2)研究了中高分辨率可见光遥感图像中感兴趣目标分割的自动化技术。现有的目标分割方法,要么是需要先验知识的有监督的自动分割,要么是无需先验知识但需要人工初始化的无监督分割。本文依据解决计算机视觉中的丢失数据问题的思路,对最大期望法的初始化方法进行改进,实现了迭代过程中参数的自动初始化,从而实现了感兴趣目标的自动分割。
(3)当感兴趣目标处在一个比较复杂的背景下,需要引入额外的附加先验才能够实现提取。论文引入了目标的形状先验。对复杂背景下的目标以受云层遮挡的海上舰船为例,通过分析获得舰船目标的形状模板作为先验,基于水平集理论的形状距离表示,将目标与复杂背景放在一起构造模型,并构造对应的能量函数,在能量最小化的过程中实现感兴趣目标的分割提取。
(4)最后,为了实现完整的可见光遥感图像上感兴趣目标的解译识别,在保证高准确率和低时间复杂度的同时,建立了一个感兴趣目标的人机交互分割提取系统。研究工作中的新贡献在于:
基于贝叶斯准则提出了城区的矢量模糊分割方法,相较于传统的多尺度分割方法,该方法快速有效,且具有较高的准确率。
对视觉上的参数估计提出了一种自动初始化方法,称之为方向标定法。使用该方法时,目标分割过程既无需初始化也无需人工参与,只依据图像自身的光谱属性和颜色属性通过自身的初始化与迭代来实现。实验验证了该方法的有效性和可靠性。
针对有云层遮挡的海上舰船提出了一种云层舰船模型(cloud cover ship model),将感兴趣目标与背景作为一个整体来看待。根据可见光遥感图像上云层是否存在阴影建立不同的模型以及对应的能量函数。实验验证了该方法的有效性。
综上,本文的研究工作首先从理论上进行分析,进而进行相应的算法设计,并通过实验验证了各个算法的有效性。
Segmentation is plays a vital role in remote sensing image processing. In the meanwhile, optical remote sensing image plays an important role in both military and civil situations. Therefore it is of great value about the research on optical remote sensing image segmentation. Existing remote objects extraction research mostly focuses on some kind of special objects in infrared images or SAR images. But the segmentation of optical remote sensing image mostly focuses on surface features landscape classification of low-middle spatial resolution optical remote sensing images. Taking the surface features landscape classification of low-middle spatial resolution optical remote sensing images as segmentation, existing methods mostly cannot get satisfied efficiency and reliability. For the surface objects with clear border and high-middle spatial resolution, the existing extraction methods are mostly depends on human experience or semi-automatic human-computer interaction. So the intelligent and automatic methods are necessary. In this paper, we study some key issues on objects segmentation and extraction in optical remote sensing images. Based on different segmentation requirements, for the low-middle spatial regional objects or high-middle small objects, different segmentation or extraction methods are proposed here. Experiment result on the actual remote sensing images gives evidence of this method’s efficiency.
In this dissertation, based on the existing segmentation research of the remote sensing image processing; our work are as follows:
Firstly, low-middle spatial resolution optical remote sensing image is taken into account. We take urban as an example of area objects and focus on the segmentation. The urban objects usually have a fuzzy boundary and Connectivity large area, so some segmentation speed is very slow. In this dissertation, a vectorized fuzzy segmentation method is proposed based on fuzzy set theory. The fuzzy membership function is constructed based on the model method and the fuzzy statistical method. The effectiveness of our method is verified by a fuzzy training process. Experiment result shows more efficient and higher accuracy compared with the traditional method.
Secondly, we consider the segmentation and extraction in middle-high resolution optical remote sensing images. The existing segmentation is either the supervised automatic segmentation with priori knowledge or the unsupervised segmentation with human initialization. A complete automization method is proposed, without initialization or human interaction. It is a data loss problem in computer vision. We do some work with the initialization of expectation maximization and propose a direction labeling method, which is used in the iterations of parameter estimization process. Experiment result shows the efficiency and reliability.
Thirdly, shape prior is introduced when the object of interest is in complicated background, such as ship objects under cloud in sea background. A cloud cover ship model is proposed based on features of optical remote sensing images. Based on the prior of the ship’s shape template, the model is constructed by putting the object and complex background together. Then the energy functions are constructed, and ship extraction is completed until the corresponding energy has been minimized. Experiment result gives evidence of this model’s efficiency.
Finally, in order to get an object interpretation in the whole optical remote sensing image, we propose a human interactive interpretation system for objects of interest, which assures high accuracy and low time complexity at the same time. Our contributions are as follows:
A vectorization fuzzy segmentation method for urban area is proposed based on the Bayes Rule. Compared with traditional multi-scale segmentation, our method is more effective and more accurate.
A direction labeling method with automatic initialization is proposed for the parameter estimation of the vision. The object segmentation process is realized with its own initialization and iteration. The process only needs the spectrum and color attributes of the image, without initialization and human interaction. Experiment result shows the efficiency and reliability.
A cloud over ship model is proposed for the ship objects under cloud in sea background. And the model is constructed by putting the object and complex background together. Different energy functions are constructed according to whether there is a cloud shadow. Experiment result shows the efficiency.
In this dissertation, some research on theory analysis is presented at the beginning, and then some corresponding algorithms are constructed. Experiment result shows the efficiency and reliability of these algorithms.
引文
[1]王明远.空间对地观测技术导论[B].北京:军事谊文出版社,2002
[2]徐青等编著,遥感影像融合与分辨率增强技术[M],北京:科学出版社,2007
[3]金国,遥感图像数字处理[M],北京:中国环境科学出版社,2006
[4]英时等编著,遥感应用分析原理与方法[M],北京:科学出版社,2003
[5] JAMES GLEE. Counter space operations for information Dominance [M]. Maxwell Air Force Base, AL: Air Univerity Press, oct.1994
[6]高光谱遥感图像特征选择和提取方法的比较——基于试验区Barrax的HyMap数据[J],干旱区地理,2006年2月
[7] C.Rother, V.Kolmogorov, A.Blake. GrabCut -- Interactive Foreground Extraction using Iterated Graph Cuts [C]. ACM Transactions on Graphics (TOG), 2004.23(3):309-314.
[8] L. A. Zadeh, Fuzzy sets [J]. Information and Control, 1965, 8:338-353
[9]谢季坚刘承平。模糊数学方法及其应用(第三版)[M],武汉:华中科技大学出版社,2006年8月
[10]基于模糊理论的图像分割方法,左奇,史忠科,西北工业大学学报,2003年06月,第21卷第3期
[11] Yan Wang and Mo Jamshidi, Fuzzy logic applied in remote sensing image classification, IEEE International Conference on systems, Man and Cybernetics, 2004
[12] Farid Melgani, Bakir A.R.Al Hashemy, and Saleem M.R.Taha, An explicit fuzzy supervised classification method for multi-spectral remote sensing images , IEEE Transations on geoscience and remote sensing, vol.38, No.1, January 2000
[13] Tomoharu Nakashima, Gaku Nakai, and Hisao Ishibuchi, proving the performance of fuzzy classification systems by membership function learning and feature selection, IEEE, 2002
[14] Ana Del Amo, Pilar Sobrevilla and Eduard Montseny, Javier Montero, Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm, IEEE, 2004
[15] Andras Bardossy and Luis Samaniego, Fuzzy rule-based classification of remotely sensed imagery, IEEE Transations on geoscience and remote sensing, vol.40, No.2, Feb.2002
[16] Ana Del Amo, Daniel Gomez and Javier Montero, Spectral fuzzy classification system fortarget recognition, IEEE, 2003
[17] Aaron K.Shackelford and Curt H.Davis, A fuzzy classification approach for high-resolution multi-spectral data over urban areas, IEEE, 2002
[18] Aaron K.Shackelford, Curt H.Davis, A hierarchical fuzzy classification approach for high-resolution multi-spectral data over urban areas, IEEE, 2003
[19] Curt H.Davis and Xiangyun Wang, Urban land cover classification from high resolution multi-spectral IKONOS imagery, IEEE, 2002
[20] Aaron K.Shackelford, Curt H.Davis, A combined fuzzy pixel-based and object-based approach for classification of high-resolution multi-spectral data over urban areas, IEEE Transations on geoscience and remote sensing, vol.41, no.10, Oct.2003
[21] Gunther Jager and Ursula C.Benz, Supervised fuzzy classification of SAR data using multiple sources, IEEE, 1999
[22] C.Castiello, G.Castellano, L.Caponetti and A.M.Fanelli, Fuzzy classification of image pixels, IEEE, 2003
[23] Elana Console, Marie Catherine Mouchot, Fuzzy classification techniques in the urban area recognition, IEEE, 1996
[24] Justin G.R.Delva, Ali M.Reza, and Robert D. Turney, FPGA implementation of a nonlinear two dimensional fuzzy filter, IEEE, 1999
[25] A.Lorenz, M.Blum, H.Ermert, Th.Senge, Comparison of different Neuro-Fuzzy classification systems for the detection of prostate cancer in ultrasonic images, IEEE, 1997
[26] Y Cheng, Mean shift, mode seeking, and clustering, IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 17, no. 8, Aug.1995: 790-799
[27] D Comaniciu, P Meer. Mean shift: A robust approach toward feature space analysis, IEEE Transactions on PAMI, vol.24, no.5, May.2002, 603-619
[28] Li XR, Wu FC, Hu ZY. Convergence of a mean shift algorithm.Journal of Software, 2005, 16(3): 365-374 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/16/365.htm
[29] M.Kass, A.Witkin, D.Terzopoulous, Snakes: active contour models[C], 1st International Conference on Computer Vision,1987, 259-268.
[30] LD Cohen and Cohen I, Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images.[J], IEEE Transactions on PAMI, 1993(15)11:1131-1147
[31] Chenyang Xu and Jerry L. Prince, radient Vector Flow: A New External Force for Snakes,IEEE Proc. Conf. on Comp. Vis. Patt. Recog. (CVPR' 97), 66-71
[32] Patrick Brigger, Jeff Hoeg, and Michael Unser, -Spline Snakes: A Flexible Tool for Parametric Contour Detection. IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 9, Sep.2000, 1484-1496
[33] A.L.Yuille, P.W.Mallinan, D.S.Cohen, Feature extraction from faces using deformable templates[J], International Journal on Computer Vision, 1992,8(2):133-144.
[34] TF Cootes, CJ Taylor, Active shape models–smart snakes, Proc. British Machine Vision Conference, 1992, 266-275
[35] Y Boykov, O Veksler, R Zabih , Fast approximate energy minimization via graph cuts, IEEE Transactions on PAMI, vol.23, no.11, 2001, pp. 1222-1239
[36] V Kwatra, A Schodl, I Essa, et al. Graphcut textures: Image and video synthesis using graph cuts[C]. ACM SIGGRAPH. 2003.22(3):277-286
[37]周红英;蔺启忠;吴昀昭等。基于AdaBoost的组合分类器在遥感影像分类中的应用[J],计算机应用研究,2007(24)10:181-184
[38]张友水,冯学智,阮仁宗等, Kohonen神经网络在遥感影像分类中的应用研究[J],遥感学报,2004(8)2:178-184
[39]吴福朝,计算机视觉中的数学方法[M]。北京:科学出版社,2008
[40] R. Jonker and A. Volgenant, A shortest augmenting path algorithm for dense and sparse linear assignment problems[J]. Computing, 1987(38)4:325-340
[41] Vladimir Kolmogorov, Ramin Zabih, What energy functions can be minimized via graph cuts?[J]. IEEE Transactions on PAMI, Sept.2004, 26(2):147-159
[42] Yuri Boykov, Vladimir Kolmogorov, Computing Geodesics and Minimal Surfaces via Graph Cuts [C], Proceedings of the Ninth IEEE International Conference on Computer Vision, p.26, Oct.2003
[43] Yuri Boykov, Marie-Pierre Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images [C]. Proceedings of Eighth IEEE ICCV, Washington: IEEE, 2001, I: 105-112
[44] Y. Boykov, M.-P. Jolly, Interactive organ segmentation using graph cuts [J]. In Medical Image Computing and Computer-Assisted Intervention, 2000, pages 276–286
[45]惠建江,刘朝晖,刘文,数学形态学在红外多弱小目标提取中的应用,光子学报, vol.35, no.4, Apr.2006, 626-629
[46]韩晓萍,付冬梅,基于形态学的遮挡目标提取的区域生长法研究,计算机应用研究, vol.24, no.10, Oct.2007, 158-160
[47]毛晓楠,周越,基于序贯蒙特卡罗方法的自适应红外目标提取算法,微型电脑应用, vol.24, no.4, 2008, 55-59
[48]杨威,李俊山,史德琴,时空联合的红外运动目标提取算法,光电工程, vol.35, no.5, May.2008, 50-54
[49]谢丽宽,李钊,胡双演,史德琴,一种新的红外目标提取算法,信号与信息处理, vol.39, no.6, 2009, 26-27
[50]王栋,陈映鹰,秦平,基于ICA和SNF的SAR机场目标提取,计算机工程, vol.35, no.24, 2009.12
[51]刘芳宇,李昱彤,杨杰,基于独立成分分析的强噪声海域SAR图点目标提取方法,昆明理工大学学报, vol.33, no.1, Feb.2008, 23-27
[52]江标初,陈映鹰,基于知识的SAR图像机场目标提取方法,计算机工程, vol.33, no.17, Sep.2007, 29-31
[53]刘开刚,许梅生,李维,一种基于双阈值区域分割的SAR图像目标提取方法,理论与方法, vol.27, no.3, Mar.2008, 3-6
[54]张旭光,韩广良,孙巍.复杂背景下运动目标的提取[J].光电工程, 2006, 33(4): 11-13.
[55]董静,覃喜庆,一种复杂背景下红外目标提取的实时性算法[J].光学与光电技术, vol.3, no.4, 2005, 26-28
[56]林玉池,崔彦平,黄银国,复杂背景下边缘提取与目标识别方法研究,光学精密工程, vol.14, no.3, 2006, 509-514
[57]权炜,郑南宁,贾新春,复杂背景下的车辆牌照字符提取方法研究,信息与控制, vol.31, no.1, Feb.2002, 25-29
[58]王程,王润生,野外复杂背景下红外图像的目标检测,红外与激光工程, vol.29, no.1, 2000, 5-8
[59]郝志成,朱明,刘微,复杂背景下目标的快速提取与跟踪,吉林大学学报:工学版, vol.36, no.2, 2006, 259-263
[60]刘永信,魏平,复杂背景图像中检测动目标的一种方法,计算机工程与应用, vol.38, no.23, 2002, 22-24
[61]邢延,张天序,复杂背景下基于知识的目标识别算法研究,模式识别与人工智能, vol.8, no.3, 1995, 237-242
[62]朱亚平,刘健文,白洁。云的光谱和纹理特征统计分析[J].遥感技术与应用Feb.2006(21)1: 18-24
[63]陈姚,王金亮,李石华,遥感图像中云层遮挡影响消除方法研究述评,国土资源遥感, no.1, Mar.2006, 61-65
[64]李存军,刘良云,王纪华,宋晓宇,王人潮,基于Landsat影像自身特征的薄云自动探测与去除,浙江大学学报, Vol.40, no.1, Jan. 2006, 10-13
[65]叶增军,王江安,阮玉,邹勇华,海空复杂背景下红外弱点目标的检测算法,红外与毫米波学报, Vol.19, No.2, 2000, 121-124
[66]梁天刚,吴彩霞,陈全功,徐宗宝,北疆牧区积雪图像分类与雪深反演模型的研究,冰川冻土, Vo1.26, No.2, Apr.2004, 160-164
[67]苏惠敏,薛亮, TM影像解译中去除云层及其阴影方法研究,西北大学学报, Vol. 35, No.6, Dec.2005, 807-810
[68] Theo Algra, On the effectiveness of cloud cover avoidance methods in support of the Superspectral Mission for Land Applications, IEEE, 2002, 982-985
[69] Mahmood R. Azimi-Sadjadi, Mukhtiar A. Sliaikh, Bin Tian , Kenneth E. Eis, and Donald Reinke, Neural Network-Based Clloud Detection/ Classification using Textural and Spectral Features, IEEE, 1996, 1105-1107
[70] Hsiao-hua Burke, SuMay Hsu, Michael Griffin, Carolyn Upham and Kris Farrar, EO-1 Hyperion Data Analysis Applicable to Cloud Detection, Coastal Characterization and Terrain Classification, IEEE, 2004, 1483-1486
[71] Zhizhang Yang, Grayson Wood, and John E. O'Reilly, Cloud Detection In Sea Surface Temperature Images By Combining Data From Noaa Polar-Orbiting And Geostationary Satellites, IEEE, 2000, 1817-1820
[72] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing [B], Beijing: Publishing House of Electronics Industry; 2002.
[73] Aaron K. Shackelford, Curt H. Davis, A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multi-Spectral Data over Urban Areas [J]. IEEE Transactions on Geo-science and Remote Sensing, October 2003.41(10): 2354-2363.
[74]赵萍,冯学智,林广发. SPOT卫星影像居民地信息自动提取的决策树方法研究[J] .遥感学报,2003.7(4): 309-315
[75] Solberg A.H.S., Jain A.K., Taxt T. Multi-source classification of remotely sensed data:fusion of Land-sat TM and SAR images.[J]. IEEE Transactions on Geo-science and Remote Sensing, July 1994.32(4):768-778.
[76]何春阳,曹鑫,史培军,李京.基于Landsat7 ETM+全色数据纹理和结构信息复合的城市建筑信息提取[J],武汉大学学报:信息科学版, 2004.29(9):800-804
[77] Zhang Q, Wang J, Gong P, et al. Study of Urban Spatial Patterns form SPOT Panchromatic Imagery Using Textural Analysis[J]. International Journal of Remote Sensing, 2003 224(21):4, 137-160
[78] Li Kai, Muller J-P, Segmenting Satellite Imagery: A Region Growing Scheme[J]. Geo-science and Remote Sensing Symposium, IGARSS '91. Remote Sensing: Global Monitoring for Earth Management. 1991.2(6):1075-1078
[79]杨泽运,康家银,赵广东,利用Quick Bird全色遥感影像更新城市大比例尺地形图[J].测绘工程, 2005.14(2):29-31.
[80]路威,张占睦,多尺度几何信息分割算法在居民的提取中的应用[J],信息工程大学学报, Jun.2003.4(2):54-57-89.
[81]文贡坚,李德仁,叶芬,从卫星遥感全色图像中自动提取城市目标[J].武汉大学学报:信息科学版,2003.28(2):212-218
[82] Ridd M K, Liu J.A. Comparison of Four Algorithms for Change Detection in an Urban Environment [J], Remote Sensing of Environment, 1998, 63(1): 95-100
[83] Dai X, Khorram S., The Effects of Image Misregistration on the Accuracy of Remotely Sensed Change Detection [J]. IEEE Trans. Geosci. Remote Sensing, 1998, 36(5): 1566-1577
[84]胡宝清编著。模糊理论基础(第一版)[M],武汉:武汉大学出版社2004年10月
[85] Giles M. Foody. Status of land cover classification accuracy assessment[J],Remote Sensing of Environment ,2002,80:185– 201
[86] Huertas A. Detect runways in complex airport scenes. Computer vision,graphics and image processing,1983,24(2):43-57
[87] Sita D. Target detection in SLAR images, Proc SPIE, 1994, 2232: 291-299
[88] Deba Prased Mandal, C.A.Murthy, Sankar K.Pal, Anakysis of IRS for detectiong man-made objects with a multivalued recognition system. IEEE transactions on systems man and cybernetics, 1996,26(2):241-247
[89]姜骊黎,史册等,遥感图像中水上桥梁的识别。模式识别与人工智能,2000, 13(2): 214-217
[90]邱志敏;李军;葛军。基于Hausdorff距离的自动目标识别算法的研究[J]。红外技术,2006(28)4:199-202
[91]丁艳,金伟其,窦建伟,基于支持向量机的自动目标识别算法[J]。光学技术,2008 (34) 5:791-793
[92]计算机视觉:一种现代方法[M],(美)David A. Forsyth,Jean Ponce著,林学,王宏等译,北京:电子工业出版社,2004.6
[93] Christophe Biernacki. Initializing EM using the properties of its trajectories in Gaussian mixtures [J], Statistics and Computing, vol.14, no.3, Aug. 2004, 267-279
[94] Christophe Biernacki, Gilles Celeux, and Gérard Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models [J], Computational Statistics & Data Analysis, vol.41, Issues 3-4, Jan.2003, 561-575
[95] A. Berlinet, and C. Roland. Parabolic acceleration of the EM algorithm [J], Statistics and Computing, vol.19, no.1, Mar. 2009, 35-47
[96]向日华,王润生,一种基于高斯混合模型的距离图像分割算法[J],软件学报,2003,14(7):1250-1257
[97] Jiang XY, Bunke H. Edge detection in range images based on scan line approximation [J]. Computer Vision and Image Understanding, 1999, 73(2):183~199.
[98] Hoffman R, Jain AK, Segment and classification of range images [J], IEEE Transactions on PAMI, 1996, 9(5):608~620.
[99]何明,冯博琴,马兆丰,傅向华,一种基于高斯混合模型的无监督粗糙聚类方法[J],哈尔滨工业大学学报,2006,38(2):256-259
[100] JAIN A K, MURTY M N, FLYNN P J. Data clustering: Review [J]. ACM Computing Surveys (CSUR), 1999,31(3) : 264 -323.
[101]岳佳,王士同,高斯混合模型聚类中EM算法及初始化的研究[J],微计算机信息,2006,22(33):244-246
[102] Yuri Boykov, and Vladimir Kolmogorov, An experimental comparison of min-cut max- flow algorithms for energy minimization in vision, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol.26, no.9, SEPT. 2004, 1124-1137
[103] Jian Yang, David Zhang, Alejandro F. Frangi, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions On Pattern Analysis And Machine Intelligence, Jan.2004, 26(1):131-137
[104] L. Zhao and Y. Yang, Theoretical Analysis of Illumination in PCA-Based Vision Systems [J], Pattern Recognition, vol.32, no.4, 1999, 547-564
[105] J. Yang, J.Y. Yang, From Image Vector to Matrix: A Straightforward Image Projection Technique—IMPCA vs. PCA [J], Pattern Recognition, vol. 35, no. 9, 2002, pp. 1997-1999
[106] S.C Yan, D. Xu, B.Y Zhang, H.J. Zhang, Q.Yang, and Stephen Lin. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction [J], IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 1, Jan. 2007, 40-51
[107] Nhat Vu and B.S. Manjunath,Shape prior segmentation of multiple objects with graph cuts[C],Computer Vision and Pattern Recognition, 2008, IEEE Conference on 23-28 June 2008 Page(s):1 - 8
[108] Olga Veksler, Star Shape Prior for Graph-Cut Image Segmentation [C], Computer Vision– ECCV 2008, 454-467
[109] Soo-Chang Pei and Chao-Nan Lin,Image normalization for pattern recognition[C],Image and Vision Computing, Dec. 1995,13(10):711-723
[110] D Shen, HHS Ip, Generalized Affine Invariant Image Normalization [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.5, May 1997, 431-440
[111]Pierre Lanchantin, Wojciech Pieczynski, Unsupervised non stationary image segmentation using triplet Markov chains[C]. Proceedings of Advanced Concepts for Intelligent Vision Systems (ACVIS 04), 2004, available on http://recherche.ircam.fr/equipes/analyse-synthese/lanchant/uploads/Main/C4.pdf
[112] S. Derrode and W. Pieczynski, SAR image segmentation using generalized Pairwise Markov Chains[C], SPIE’s International Symposium on Remote Sensing, September 22-27, Crete, Greece, 2002.
[113] S. Derrode and W. Pieczynski, Signal and image restoration using Pairwise Markov Chains[J], IEEE Trans. on Signal Processing, September 2004.
[114]姬渊,秦志远,毛丽,董峡,无控制点条件下SPOT5卫星影像海上目标定位技术[J],海洋测绘,2008(28)5:20-22
[115]吴琦颖,李翠华.一种新颖的海上运动目标实时检测方法[J].计算机工程与应用,2007,43(14):213-216
[116]孙红光,卜倩,李欢利,张瑾,张慧杰。基于OTSU分割的云层背景下弱目标检测算法研究[J],东北师大学报(自然科学版),2009(41)2:79-83
[117]蒋运辉,陈怀新。基于小波方向滤波的有云层遥感图像舰船检测方法[J],电讯技术,2008(48)1:90-93
[118] Frost V S, et al., A model for radar images and its application to adaptive digital filtering of multiplicative noise[J], IEEE Trans. on Pattern Analysis and Machine Intelligence, 1982, (4): 157-166.
[119]秦其明.遥感图像自动解译面临的问题与解决的途径.[J]测绘科学.2000.25(2): 21-24.
[120]周忠发,黄路迦,肖丹.贵州高原喀斯特石漠化遥感调查研究—以贵州省清镇市为例. [J]贵州地质.2001.18(2):93-98.
[121]刘亚岚,阎守邕,王涛.遥感图像人机交互判读方法研究及其应用.[J]地理与地理信息科学. 2003.19(1):27-31.
[122] Q Tian, Y Wu, TS Huang, Combine user defined region-of-interest and spatial layout for image retrieval [C], Int. Conf. on Image Processing, Sep. 2000, vol.3, pp. 746-749
[123] Claudio M. Privitera and Lawrence W. Stark, Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations [J], IEEE transactions on pattern analysis and machine intelligence, vol. 22, no. 9, Sep. 2000, 970-982
[2]徐青等编著,遥感影像融合与分辨率增强技术[M],北京:科学出版社,2007
[3]金国,遥感图像数字处理[M],北京:中国环境科学出版社,2006
[4]英时等编著,遥感应用分析原理与方法[M],北京:科学出版社,2003
[5] JAMES GLEE. Counter space operations for information Dominance [M]. Maxwell Air Force Base, AL: Air Univerity Press, oct.1994
[6]高光谱遥感图像特征选择和提取方法的比较——基于试验区Barrax的HyMap数据[J],干旱区地理,2006年2月
[7] C.Rother, V.Kolmogorov, A.Blake. GrabCut -- Interactive Foreground Extraction using Iterated Graph Cuts [C]. ACM Transactions on Graphics (TOG), 2004.23(3):309-314.
[8] L. A. Zadeh, Fuzzy sets [J]. Information and Control, 1965, 8:338-353
[9]谢季坚刘承平。模糊数学方法及其应用(第三版)[M],武汉:华中科技大学出版社,2006年8月
[10]基于模糊理论的图像分割方法,左奇,史忠科,西北工业大学学报,2003年06月,第21卷第3期
[11] Yan Wang and Mo Jamshidi, Fuzzy logic applied in remote sensing image classification, IEEE International Conference on systems, Man and Cybernetics, 2004
[12] Farid Melgani, Bakir A.R.Al Hashemy, and Saleem M.R.Taha, An explicit fuzzy supervised classification method for multi-spectral remote sensing images , IEEE Transations on geoscience and remote sensing, vol.38, No.1, January 2000
[13] Tomoharu Nakashima, Gaku Nakai, and Hisao Ishibuchi, proving the performance of fuzzy classification systems by membership function learning and feature selection, IEEE, 2002
[14] Ana Del Amo, Pilar Sobrevilla and Eduard Montseny, Javier Montero, Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm, IEEE, 2004
[15] Andras Bardossy and Luis Samaniego, Fuzzy rule-based classification of remotely sensed imagery, IEEE Transations on geoscience and remote sensing, vol.40, No.2, Feb.2002
[16] Ana Del Amo, Daniel Gomez and Javier Montero, Spectral fuzzy classification system fortarget recognition, IEEE, 2003
[17] Aaron K.Shackelford and Curt H.Davis, A fuzzy classification approach for high-resolution multi-spectral data over urban areas, IEEE, 2002
[18] Aaron K.Shackelford, Curt H.Davis, A hierarchical fuzzy classification approach for high-resolution multi-spectral data over urban areas, IEEE, 2003
[19] Curt H.Davis and Xiangyun Wang, Urban land cover classification from high resolution multi-spectral IKONOS imagery, IEEE, 2002
[20] Aaron K.Shackelford, Curt H.Davis, A combined fuzzy pixel-based and object-based approach for classification of high-resolution multi-spectral data over urban areas, IEEE Transations on geoscience and remote sensing, vol.41, no.10, Oct.2003
[21] Gunther Jager and Ursula C.Benz, Supervised fuzzy classification of SAR data using multiple sources, IEEE, 1999
[22] C.Castiello, G.Castellano, L.Caponetti and A.M.Fanelli, Fuzzy classification of image pixels, IEEE, 2003
[23] Elana Console, Marie Catherine Mouchot, Fuzzy classification techniques in the urban area recognition, IEEE, 1996
[24] Justin G.R.Delva, Ali M.Reza, and Robert D. Turney, FPGA implementation of a nonlinear two dimensional fuzzy filter, IEEE, 1999
[25] A.Lorenz, M.Blum, H.Ermert, Th.Senge, Comparison of different Neuro-Fuzzy classification systems for the detection of prostate cancer in ultrasonic images, IEEE, 1997
[26] Y Cheng, Mean shift, mode seeking, and clustering, IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 17, no. 8, Aug.1995: 790-799
[27] D Comaniciu, P Meer. Mean shift: A robust approach toward feature space analysis, IEEE Transactions on PAMI, vol.24, no.5, May.2002, 603-619
[28] Li XR, Wu FC, Hu ZY. Convergence of a mean shift algorithm.Journal of Software, 2005, 16(3): 365-374 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/16/365.htm
[29] M.Kass, A.Witkin, D.Terzopoulous, Snakes: active contour models[C], 1st International Conference on Computer Vision,1987, 259-268.
[30] LD Cohen and Cohen I, Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images.[J], IEEE Transactions on PAMI, 1993(15)11:1131-1147
[31] Chenyang Xu and Jerry L. Prince, radient Vector Flow: A New External Force for Snakes,IEEE Proc. Conf. on Comp. Vis. Patt. Recog. (CVPR' 97), 66-71
[32] Patrick Brigger, Jeff Hoeg, and Michael Unser, -Spline Snakes: A Flexible Tool for Parametric Contour Detection. IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 9, Sep.2000, 1484-1496
[33] A.L.Yuille, P.W.Mallinan, D.S.Cohen, Feature extraction from faces using deformable templates[J], International Journal on Computer Vision, 1992,8(2):133-144.
[34] TF Cootes, CJ Taylor, Active shape models–smart snakes, Proc. British Machine Vision Conference, 1992, 266-275
[35] Y Boykov, O Veksler, R Zabih , Fast approximate energy minimization via graph cuts, IEEE Transactions on PAMI, vol.23, no.11, 2001, pp. 1222-1239
[36] V Kwatra, A Schodl, I Essa, et al. Graphcut textures: Image and video synthesis using graph cuts[C]. ACM SIGGRAPH. 2003.22(3):277-286
[37]周红英;蔺启忠;吴昀昭等。基于AdaBoost的组合分类器在遥感影像分类中的应用[J],计算机应用研究,2007(24)10:181-184
[38]张友水,冯学智,阮仁宗等, Kohonen神经网络在遥感影像分类中的应用研究[J],遥感学报,2004(8)2:178-184
[39]吴福朝,计算机视觉中的数学方法[M]。北京:科学出版社,2008
[40] R. Jonker and A. Volgenant, A shortest augmenting path algorithm for dense and sparse linear assignment problems[J]. Computing, 1987(38)4:325-340
[41] Vladimir Kolmogorov, Ramin Zabih, What energy functions can be minimized via graph cuts?[J]. IEEE Transactions on PAMI, Sept.2004, 26(2):147-159
[42] Yuri Boykov, Vladimir Kolmogorov, Computing Geodesics and Minimal Surfaces via Graph Cuts [C], Proceedings of the Ninth IEEE International Conference on Computer Vision, p.26, Oct.2003
[43] Yuri Boykov, Marie-Pierre Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images [C]. Proceedings of Eighth IEEE ICCV, Washington: IEEE, 2001, I: 105-112
[44] Y. Boykov, M.-P. Jolly, Interactive organ segmentation using graph cuts [J]. In Medical Image Computing and Computer-Assisted Intervention, 2000, pages 276–286
[45]惠建江,刘朝晖,刘文,数学形态学在红外多弱小目标提取中的应用,光子学报, vol.35, no.4, Apr.2006, 626-629
[46]韩晓萍,付冬梅,基于形态学的遮挡目标提取的区域生长法研究,计算机应用研究, vol.24, no.10, Oct.2007, 158-160
[47]毛晓楠,周越,基于序贯蒙特卡罗方法的自适应红外目标提取算法,微型电脑应用, vol.24, no.4, 2008, 55-59
[48]杨威,李俊山,史德琴,时空联合的红外运动目标提取算法,光电工程, vol.35, no.5, May.2008, 50-54
[49]谢丽宽,李钊,胡双演,史德琴,一种新的红外目标提取算法,信号与信息处理, vol.39, no.6, 2009, 26-27
[50]王栋,陈映鹰,秦平,基于ICA和SNF的SAR机场目标提取,计算机工程, vol.35, no.24, 2009.12
[51]刘芳宇,李昱彤,杨杰,基于独立成分分析的强噪声海域SAR图点目标提取方法,昆明理工大学学报, vol.33, no.1, Feb.2008, 23-27
[52]江标初,陈映鹰,基于知识的SAR图像机场目标提取方法,计算机工程, vol.33, no.17, Sep.2007, 29-31
[53]刘开刚,许梅生,李维,一种基于双阈值区域分割的SAR图像目标提取方法,理论与方法, vol.27, no.3, Mar.2008, 3-6
[54]张旭光,韩广良,孙巍.复杂背景下运动目标的提取[J].光电工程, 2006, 33(4): 11-13.
[55]董静,覃喜庆,一种复杂背景下红外目标提取的实时性算法[J].光学与光电技术, vol.3, no.4, 2005, 26-28
[56]林玉池,崔彦平,黄银国,复杂背景下边缘提取与目标识别方法研究,光学精密工程, vol.14, no.3, 2006, 509-514
[57]权炜,郑南宁,贾新春,复杂背景下的车辆牌照字符提取方法研究,信息与控制, vol.31, no.1, Feb.2002, 25-29
[58]王程,王润生,野外复杂背景下红外图像的目标检测,红外与激光工程, vol.29, no.1, 2000, 5-8
[59]郝志成,朱明,刘微,复杂背景下目标的快速提取与跟踪,吉林大学学报:工学版, vol.36, no.2, 2006, 259-263
[60]刘永信,魏平,复杂背景图像中检测动目标的一种方法,计算机工程与应用, vol.38, no.23, 2002, 22-24
[61]邢延,张天序,复杂背景下基于知识的目标识别算法研究,模式识别与人工智能, vol.8, no.3, 1995, 237-242
[62]朱亚平,刘健文,白洁。云的光谱和纹理特征统计分析[J].遥感技术与应用Feb.2006(21)1: 18-24
[63]陈姚,王金亮,李石华,遥感图像中云层遮挡影响消除方法研究述评,国土资源遥感, no.1, Mar.2006, 61-65
[64]李存军,刘良云,王纪华,宋晓宇,王人潮,基于Landsat影像自身特征的薄云自动探测与去除,浙江大学学报, Vol.40, no.1, Jan. 2006, 10-13
[65]叶增军,王江安,阮玉,邹勇华,海空复杂背景下红外弱点目标的检测算法,红外与毫米波学报, Vol.19, No.2, 2000, 121-124
[66]梁天刚,吴彩霞,陈全功,徐宗宝,北疆牧区积雪图像分类与雪深反演模型的研究,冰川冻土, Vo1.26, No.2, Apr.2004, 160-164
[67]苏惠敏,薛亮, TM影像解译中去除云层及其阴影方法研究,西北大学学报, Vol. 35, No.6, Dec.2005, 807-810
[68] Theo Algra, On the effectiveness of cloud cover avoidance methods in support of the Superspectral Mission for Land Applications, IEEE, 2002, 982-985
[69] Mahmood R. Azimi-Sadjadi, Mukhtiar A. Sliaikh, Bin Tian , Kenneth E. Eis, and Donald Reinke, Neural Network-Based Clloud Detection/ Classification using Textural and Spectral Features, IEEE, 1996, 1105-1107
[70] Hsiao-hua Burke, SuMay Hsu, Michael Griffin, Carolyn Upham and Kris Farrar, EO-1 Hyperion Data Analysis Applicable to Cloud Detection, Coastal Characterization and Terrain Classification, IEEE, 2004, 1483-1486
[71] Zhizhang Yang, Grayson Wood, and John E. O'Reilly, Cloud Detection In Sea Surface Temperature Images By Combining Data From Noaa Polar-Orbiting And Geostationary Satellites, IEEE, 2000, 1817-1820
[72] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing [B], Beijing: Publishing House of Electronics Industry; 2002.
[73] Aaron K. Shackelford, Curt H. Davis, A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multi-Spectral Data over Urban Areas [J]. IEEE Transactions on Geo-science and Remote Sensing, October 2003.41(10): 2354-2363.
[74]赵萍,冯学智,林广发. SPOT卫星影像居民地信息自动提取的决策树方法研究[J] .遥感学报,2003.7(4): 309-315
[75] Solberg A.H.S., Jain A.K., Taxt T. Multi-source classification of remotely sensed data:fusion of Land-sat TM and SAR images.[J]. IEEE Transactions on Geo-science and Remote Sensing, July 1994.32(4):768-778.
[76]何春阳,曹鑫,史培军,李京.基于Landsat7 ETM+全色数据纹理和结构信息复合的城市建筑信息提取[J],武汉大学学报:信息科学版, 2004.29(9):800-804
[77] Zhang Q, Wang J, Gong P, et al. Study of Urban Spatial Patterns form SPOT Panchromatic Imagery Using Textural Analysis[J]. International Journal of Remote Sensing, 2003 224(21):4, 137-160
[78] Li Kai, Muller J-P, Segmenting Satellite Imagery: A Region Growing Scheme[J]. Geo-science and Remote Sensing Symposium, IGARSS '91. Remote Sensing: Global Monitoring for Earth Management. 1991.2(6):1075-1078
[79]杨泽运,康家银,赵广东,利用Quick Bird全色遥感影像更新城市大比例尺地形图[J].测绘工程, 2005.14(2):29-31.
[80]路威,张占睦,多尺度几何信息分割算法在居民的提取中的应用[J],信息工程大学学报, Jun.2003.4(2):54-57-89.
[81]文贡坚,李德仁,叶芬,从卫星遥感全色图像中自动提取城市目标[J].武汉大学学报:信息科学版,2003.28(2):212-218
[82] Ridd M K, Liu J.A. Comparison of Four Algorithms for Change Detection in an Urban Environment [J], Remote Sensing of Environment, 1998, 63(1): 95-100
[83] Dai X, Khorram S., The Effects of Image Misregistration on the Accuracy of Remotely Sensed Change Detection [J]. IEEE Trans. Geosci. Remote Sensing, 1998, 36(5): 1566-1577
[84]胡宝清编著。模糊理论基础(第一版)[M],武汉:武汉大学出版社2004年10月
[85] Giles M. Foody. Status of land cover classification accuracy assessment[J],Remote Sensing of Environment ,2002,80:185– 201
[86] Huertas A. Detect runways in complex airport scenes. Computer vision,graphics and image processing,1983,24(2):43-57
[87] Sita D. Target detection in SLAR images, Proc SPIE, 1994, 2232: 291-299
[88] Deba Prased Mandal, C.A.Murthy, Sankar K.Pal, Anakysis of IRS for detectiong man-made objects with a multivalued recognition system. IEEE transactions on systems man and cybernetics, 1996,26(2):241-247
[89]姜骊黎,史册等,遥感图像中水上桥梁的识别。模式识别与人工智能,2000, 13(2): 214-217
[90]邱志敏;李军;葛军。基于Hausdorff距离的自动目标识别算法的研究[J]。红外技术,2006(28)4:199-202
[91]丁艳,金伟其,窦建伟,基于支持向量机的自动目标识别算法[J]。光学技术,2008 (34) 5:791-793
[92]计算机视觉:一种现代方法[M],(美)David A. Forsyth,Jean Ponce著,林学,王宏等译,北京:电子工业出版社,2004.6
[93] Christophe Biernacki. Initializing EM using the properties of its trajectories in Gaussian mixtures [J], Statistics and Computing, vol.14, no.3, Aug. 2004, 267-279
[94] Christophe Biernacki, Gilles Celeux, and Gérard Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models [J], Computational Statistics & Data Analysis, vol.41, Issues 3-4, Jan.2003, 561-575
[95] A. Berlinet, and C. Roland. Parabolic acceleration of the EM algorithm [J], Statistics and Computing, vol.19, no.1, Mar. 2009, 35-47
[96]向日华,王润生,一种基于高斯混合模型的距离图像分割算法[J],软件学报,2003,14(7):1250-1257
[97] Jiang XY, Bunke H. Edge detection in range images based on scan line approximation [J]. Computer Vision and Image Understanding, 1999, 73(2):183~199.
[98] Hoffman R, Jain AK, Segment and classification of range images [J], IEEE Transactions on PAMI, 1996, 9(5):608~620.
[99]何明,冯博琴,马兆丰,傅向华,一种基于高斯混合模型的无监督粗糙聚类方法[J],哈尔滨工业大学学报,2006,38(2):256-259
[100] JAIN A K, MURTY M N, FLYNN P J. Data clustering: Review [J]. ACM Computing Surveys (CSUR), 1999,31(3) : 264 -323.
[101]岳佳,王士同,高斯混合模型聚类中EM算法及初始化的研究[J],微计算机信息,2006,22(33):244-246
[102] Yuri Boykov, and Vladimir Kolmogorov, An experimental comparison of min-cut max- flow algorithms for energy minimization in vision, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol.26, no.9, SEPT. 2004, 1124-1137
[103] Jian Yang, David Zhang, Alejandro F. Frangi, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions On Pattern Analysis And Machine Intelligence, Jan.2004, 26(1):131-137
[104] L. Zhao and Y. Yang, Theoretical Analysis of Illumination in PCA-Based Vision Systems [J], Pattern Recognition, vol.32, no.4, 1999, 547-564
[105] J. Yang, J.Y. Yang, From Image Vector to Matrix: A Straightforward Image Projection Technique—IMPCA vs. PCA [J], Pattern Recognition, vol. 35, no. 9, 2002, pp. 1997-1999
[106] S.C Yan, D. Xu, B.Y Zhang, H.J. Zhang, Q.Yang, and Stephen Lin. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction [J], IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 1, Jan. 2007, 40-51
[107] Nhat Vu and B.S. Manjunath,Shape prior segmentation of multiple objects with graph cuts[C],Computer Vision and Pattern Recognition, 2008, IEEE Conference on 23-28 June 2008 Page(s):1 - 8
[108] Olga Veksler, Star Shape Prior for Graph-Cut Image Segmentation [C], Computer Vision– ECCV 2008, 454-467
[109] Soo-Chang Pei and Chao-Nan Lin,Image normalization for pattern recognition[C],Image and Vision Computing, Dec. 1995,13(10):711-723
[110] D Shen, HHS Ip, Generalized Affine Invariant Image Normalization [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.5, May 1997, 431-440
[111]Pierre Lanchantin, Wojciech Pieczynski, Unsupervised non stationary image segmentation using triplet Markov chains[C]. Proceedings of Advanced Concepts for Intelligent Vision Systems (ACVIS 04), 2004, available on http://recherche.ircam.fr/equipes/analyse-synthese/lanchant/uploads/Main/C4.pdf
[112] S. Derrode and W. Pieczynski, SAR image segmentation using generalized Pairwise Markov Chains[C], SPIE’s International Symposium on Remote Sensing, September 22-27, Crete, Greece, 2002.
[113] S. Derrode and W. Pieczynski, Signal and image restoration using Pairwise Markov Chains[J], IEEE Trans. on Signal Processing, September 2004.
[114]姬渊,秦志远,毛丽,董峡,无控制点条件下SPOT5卫星影像海上目标定位技术[J],海洋测绘,2008(28)5:20-22
[115]吴琦颖,李翠华.一种新颖的海上运动目标实时检测方法[J].计算机工程与应用,2007,43(14):213-216
[116]孙红光,卜倩,李欢利,张瑾,张慧杰。基于OTSU分割的云层背景下弱目标检测算法研究[J],东北师大学报(自然科学版),2009(41)2:79-83
[117]蒋运辉,陈怀新。基于小波方向滤波的有云层遥感图像舰船检测方法[J],电讯技术,2008(48)1:90-93
[118] Frost V S, et al., A model for radar images and its application to adaptive digital filtering of multiplicative noise[J], IEEE Trans. on Pattern Analysis and Machine Intelligence, 1982, (4): 157-166.
[119]秦其明.遥感图像自动解译面临的问题与解决的途径.[J]测绘科学.2000.25(2): 21-24.
[120]周忠发,黄路迦,肖丹.贵州高原喀斯特石漠化遥感调查研究—以贵州省清镇市为例. [J]贵州地质.2001.18(2):93-98.
[121]刘亚岚,阎守邕,王涛.遥感图像人机交互判读方法研究及其应用.[J]地理与地理信息科学. 2003.19(1):27-31.
[122] Q Tian, Y Wu, TS Huang, Combine user defined region-of-interest and spatial layout for image retrieval [C], Int. Conf. on Image Processing, Sep. 2000, vol.3, pp. 746-749
[123] Claudio M. Privitera and Lawrence W. Stark, Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations [J], IEEE transactions on pattern analysis and machine intelligence, vol. 22, no. 9, Sep. 2000, 970-982
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |