高空间分辨率遥感影像自适应分割方法研究
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摘要
随着遥感对地观测技术的不断进步，遥感影像的时、空、谱分辨率越来越高，影像数据量爆炸与处理能力严重滞后的矛盾日益尖锐，传统的影像处理技术面临新的挑战，发展更加高效的遥感影像数据挖掘理论与技术方法成为应对这一挑战的一种趋势。面向对象的遥感影像分析正是在此过程中逐渐形成的一个研究领域，影像分割是其中的一项关键技术，也是面向对象遥感影像分析、理解与应用中的难点。现有文献中介绍的遥感影像分割方法以及集成在商用遥感影像处理软件平台中的相关算法均存在不同程度的局限性，本文针对现有研究工作中的不足，探索高空间分辨率遥感影像的自适应分割方法，主要研究内容与成果如下：
（1）归纳总结遥感影像分割研究领域现有的代表性研究成果（重点介绍现有主流遥感影像分割方法分类谱系中的各种方法），指出该领域目前存在的主要问题（分割尺度参数模型的适应性差、多尺度分割中对象特征信息的利用与耦合不足、分割算法缺乏自适应性、多尺度分割对象与语义影像目标不一致）与未来的发展趋势。
（2）研究高空间分辨率遥感影像预处理步骤中融合和滤波技术与分割效果的关系，以QuickBird影像为样本数据检验通过影像融合实现高空间分辨率遥感影像全色波段与多光谱波段优势互补、通过影像滤波抑制语义影像目标内部光谱异质性与同时保持边缘信息的可行性，探索基于分割效果评价与选择融合和滤波处理方法的新思路。
（3）基于输出融合策略与Canny算子，提出适用于高空间分辨率遥感影像的矢量、加权矢量与标量边缘检测新算法，并利用该算法在多色彩空间中实现了高空间分辨率遥感影像边缘信息的有效提取，同时研究不同地物类型对边缘检测响应程度的差异以及该差异对边缘提取的影响。
（4）面向高空间分辨率遥感影像数据多色彩空间分割的特点，对均值漂移算法进行改进，实现了多色彩空间空-值域联合的多尺度均值漂移算法，分析比较该算法在不同色彩空间中分割高空间分辨率遥感影像的效果，验证了RGB与IHS色彩空间所具有的相对优势。
（5）基于嵌入式集成策略，提出并实现了集成边缘与区域信息、空-值域联合的自适应多尺度均值漂移算法（Adaptive Integration of Canny and Mean Shift Segmentationalgorithm，AICMS）。分别以GeoEye、QuickBird以及航空影像三种高空间分辨率遥感影像为样本数据验证该算法对高空间分辨率影像数据类型的普适性及其在自适应性方面相对于商业软件eCognition中所集成的多尺度分割算法的优势。
With the rapid development of remote sensing technology, temporal, spatial and spectralresolutions of Remotely Sensed Imagery (RSI) are higher and higher. The contradictionbetween image data explosion and serious lag in capabilities for dealing with them isincreasingly acute, which challenges traditional image processing techniques. Developing moreefficient theory and technical methods for mining RSI data becomes a tendency to address thechallenge, during which a promising research field, object-oriented RSI analysis was graduallyformed. Image segmentation is one of the key technologies in object-oriented RSI analysis,which is also the hardship of object-oriented image analysis, understanding and application. Sofar, the achieved Remotely Sensed Imagery Segmentation Methods (RSISM) still have variouslimitations and defects. In view of this, the thesis explores adaptive segmentation methods forHigh Spatial Resolution Remotely Sensed Imagery (HSRRSI). Its main research contents andrelated achievements are as follows:
(1) Presently available representative achievements in the research field of RSIsegmentation were reviewed and summarized (focusing on various RSISM in mainstreamcategory pedigree), the main current problems (including the poor adaptability of segmentationscale parameter model, the short of utilizing and coupling of object feature information inmulti-scale segmentation, the lack of self-adaptability of algorithms, the inconsistent betweenimage objects by multi-scale segmentation and their corresponding semantic image objects) andpossible researching trend of which were also put forward.
(2) The relationship of segmentation effect and fusion in conjunction with filter technologyin HSRRSI preprocessing steps was studied. By taking QuickBird image as sample data in casestudy, respectively verified was the feasibility of integrating advantages of panchromatic andmulti-spectral data of HSRRSI by fusing, and smoothing spectral heterogeneity within semanticimage objects and simultaneously keeping its edge information by filtering. A new idea, whichevaluates and selects image fusion and filter methods based on segmentation effect, has alsobeen explored.
(3) By means of output fusion strategy and Canny operator, it proposed new edge detectionalgorithms respectively based on vector, and weighted vector or scalar, which were suitable forHSRRSI. In the multi-colorspace, the algorithms were employed to effectively extract edgeinformation from HSRRSI. Also presented were the differences of spectral responsiveness among various kinds of land cover types and their impacts on edge extraction.
(4) Under considering characteristics of segmentation of HSRRSI data in multi-colorspace,the mean shift algorithm was improved, based on which the multi-colorspace,spatial-range-union, and multi-scale mean shift algorithm was implemented. The segmentationresults derived from the algorithm in different colorspace were analyzed and compared witheach other, and which verified the relative advantages of RGB and IHS colorspace.
(5) Based on embedded integration strategy, a segmentation algorithm, named AdaptiveIntegration of Canny and Mean Shift Segmentation algorithm (AICMS), of adaptive multi-scale,spatial-range-union, and integrating edge and region was proposed and implemented. Byrespectively taking GeoEye, QuickBird and aerial photography images as sample data, theuniversality was proved. Also proved was the relative advantages of AICMS algorithm inadaptive property superior to the multi-scale segmentation algorithm embedded in eCognition(a commercialized software platform).
（1）归纳总结遥感影像分割研究领域现有的代表性研究成果（重点介绍现有主流遥感影像分割方法分类谱系中的各种方法），指出该领域目前存在的主要问题（分割尺度参数模型的适应性差、多尺度分割中对象特征信息的利用与耦合不足、分割算法缺乏自适应性、多尺度分割对象与语义影像目标不一致）与未来的发展趋势。
（2）研究高空间分辨率遥感影像预处理步骤中融合和滤波技术与分割效果的关系，以QuickBird影像为样本数据检验通过影像融合实现高空间分辨率遥感影像全色波段与多光谱波段优势互补、通过影像滤波抑制语义影像目标内部光谱异质性与同时保持边缘信息的可行性，探索基于分割效果评价与选择融合和滤波处理方法的新思路。
（3）基于输出融合策略与Canny算子，提出适用于高空间分辨率遥感影像的矢量、加权矢量与标量边缘检测新算法，并利用该算法在多色彩空间中实现了高空间分辨率遥感影像边缘信息的有效提取，同时研究不同地物类型对边缘检测响应程度的差异以及该差异对边缘提取的影响。
（4）面向高空间分辨率遥感影像数据多色彩空间分割的特点，对均值漂移算法进行改进，实现了多色彩空间空-值域联合的多尺度均值漂移算法，分析比较该算法在不同色彩空间中分割高空间分辨率遥感影像的效果，验证了RGB与IHS色彩空间所具有的相对优势。
（5）基于嵌入式集成策略，提出并实现了集成边缘与区域信息、空-值域联合的自适应多尺度均值漂移算法（Adaptive Integration of Canny and Mean Shift Segmentationalgorithm，AICMS）。分别以GeoEye、QuickBird以及航空影像三种高空间分辨率遥感影像为样本数据验证该算法对高空间分辨率影像数据类型的普适性及其在自适应性方面相对于商业软件eCognition中所集成的多尺度分割算法的优势。
With the rapid development of remote sensing technology, temporal, spatial and spectralresolutions of Remotely Sensed Imagery (RSI) are higher and higher. The contradictionbetween image data explosion and serious lag in capabilities for dealing with them isincreasingly acute, which challenges traditional image processing techniques. Developing moreefficient theory and technical methods for mining RSI data becomes a tendency to address thechallenge, during which a promising research field, object-oriented RSI analysis was graduallyformed. Image segmentation is one of the key technologies in object-oriented RSI analysis,which is also the hardship of object-oriented image analysis, understanding and application. Sofar, the achieved Remotely Sensed Imagery Segmentation Methods (RSISM) still have variouslimitations and defects. In view of this, the thesis explores adaptive segmentation methods forHigh Spatial Resolution Remotely Sensed Imagery (HSRRSI). Its main research contents andrelated achievements are as follows:
(1) Presently available representative achievements in the research field of RSIsegmentation were reviewed and summarized (focusing on various RSISM in mainstreamcategory pedigree), the main current problems (including the poor adaptability of segmentationscale parameter model, the short of utilizing and coupling of object feature information inmulti-scale segmentation, the lack of self-adaptability of algorithms, the inconsistent betweenimage objects by multi-scale segmentation and their corresponding semantic image objects) andpossible researching trend of which were also put forward.
(2) The relationship of segmentation effect and fusion in conjunction with filter technologyin HSRRSI preprocessing steps was studied. By taking QuickBird image as sample data in casestudy, respectively verified was the feasibility of integrating advantages of panchromatic andmulti-spectral data of HSRRSI by fusing, and smoothing spectral heterogeneity within semanticimage objects and simultaneously keeping its edge information by filtering. A new idea, whichevaluates and selects image fusion and filter methods based on segmentation effect, has alsobeen explored.
(3) By means of output fusion strategy and Canny operator, it proposed new edge detectionalgorithms respectively based on vector, and weighted vector or scalar, which were suitable forHSRRSI. In the multi-colorspace, the algorithms were employed to effectively extract edgeinformation from HSRRSI. Also presented were the differences of spectral responsiveness among various kinds of land cover types and their impacts on edge extraction.
(4) Under considering characteristics of segmentation of HSRRSI data in multi-colorspace,the mean shift algorithm was improved, based on which the multi-colorspace,spatial-range-union, and multi-scale mean shift algorithm was implemented. The segmentationresults derived from the algorithm in different colorspace were analyzed and compared witheach other, and which verified the relative advantages of RGB and IHS colorspace.
(5) Based on embedded integration strategy, a segmentation algorithm, named AdaptiveIntegration of Canny and Mean Shift Segmentation algorithm (AICMS), of adaptive multi-scale,spatial-range-union, and integrating edge and region was proposed and implemented. Byrespectively taking GeoEye, QuickBird and aerial photography images as sample data, theuniversality was proved. Also proved was the relative advantages of AICMS algorithm inadaptive property superior to the multi-scale segmentation algorithm embedded in eCognition(a commercialized software platform).
引文
[1]周成虎，骆剑承．高分辨率卫星遥感影像地学计算．北京：科学出版社，2009．
[2]陈秋晓．高分辨率遥感影像分割方法研究：［博士学位论文］．北京：中国科学院研究生院，2004．
[3]刘建华，毛政元．高空间分辨率光学遥感影像分割方法研究综述．遥感信息，2009，6：95-101．
[4] T Blaschke, J Strobl. What’s wrong with pixels? Some recent developments interfacing remote sensingand GIS. GeoBIT/GIS:2001,12-17.
[5] A Lobo, O Chic, A Casterad. Classification of Mediterranean Crops with Multisensor Data: Per-Pixelversus Per-Object Statistics and Image Segmentation. International Journal of Remote Sensing.1996,17:2358-2400.
[6] T Blaschke, S Lang, E Lorup, et al. Object-oriented image processing in an integrated GIS/remote sensingenvironment and perspectives for environmental applications. Environmental Information for Planning,2000,2:555-570.
[7] M Baatz, A Sch pe. Multiresolution Segmentation–an Optimization Approach for High QualityMulti-scale Image Segmentation. In: STROBL, J. et al.(Hrsg.): Angewandte GeographischeInformationsverarbeitung XII. Beitr ge zum AGIT-Symposium Salzburg2000, Karlsruhe, Herbert WichmannVerlag:12–23.
[8] J Schiewe, L Tufte, M Ehlers. Potential and problems of multi-scale segmentation methods in remotesensing.GIS–Zeitschrift für Geoinformations system,2001,(6):34-39.
[9] S Giada, T D Groeve, D Ehrlich. Information extraction from very high resolution satellite imagery overLukole refugee camp, Tanzania. International Journal of Remote Sensing,2003,24(22):4251–4266.
[10] Wang Z, Wei W, Zhao S. Object-oriented classification and application in land use classification usingSPOT-5PAN imagery. In: International Geoscience and Remote Sensing Symposium (IGARSS), v5,2004:3158-3160.
[11] Hurd J D, Civco D L, Gilmore M S. Tidal Wetland Classification from Landsat Imagery Using anIntegrated Pixel-Based and Object-Based Classification Approach. ASPRS2006Annual Conference, Reno,Nevada,2006.
[12]章毓晋．图象分割．北京:科学出版社，2001，36-58．
[13]蔡银桥，毛政元．基于多特征对象的高分辨率遥感影像分类方法及其应用．国土资源感，2007，(9)：77-81．
[14]宫鹏，黎夏，徐冰．高分辨率影像解译理论与应用方法中的一些研究问题．遥感学报，2006，10(1)：1-5．
[15] K S Fu, J K Mui. A survey on image segmentation. Pattern Recognition,1981,13:3-16.
[16] S K Pal. A review on image segmentation techniques. Pattern Recognition29(1993):1277-1294.
[17] Wladyslaw Skarbek, Andreas Koschan. Colour Image Segmentation-A Survey. October1994.http://imaging.utk.edu/~koschan/paper/coseg.pdf.
[18]罗希平，田捷，诸葛婴，等．图像分割方法综述．模式识别与人工智能，1999，12(3)：300-312．
[19]王爱民，沈兰荪．图像分割研究综述．测控技术，2000，19(5)：20-24．
[20] H D Cheng, X H Jiang, Y Sun, et al. Color image segmentation: advances and prospects. PatternRecognition,2001,34(12):2259-2281.
[21]林瑶，田捷．医学图像分割方法综述．模式识别与人工智能，2002，15(2)：192-204．
[22]林开颜，吴军，徐立鸿．彩色图像分割方法综述．中国图象图形学报．2005，10(1)：1-10．
[23] Yu Jin Zhang. Advances in Image and Video Segmentation: An Overview of Image and VideoSegmentation in the Last40Years.2006.http://www.ee.tsinghua.edu.cn/~zhangyujin/3_5.htm
[24] P K Sahoo, S Soltani, A K C Wong, et al. A survey of thresholding techniques. Computer Vision,Graphics, and Image Processing,1988,41(2):233-260.
[25] Mehmet Sezgin, Bulent Sankur.Survey over image thresholding techniques and quantitative performanceevaluation. Journal of Electronic Imaging,2004,13(1):146–165.
[26] Bonsiepen L, Coy W. Stable segmentation using color information. In Computer Analysis of Images andPatterns, ed. R. Klette, Proc.of CAIP'91, Dresden, September17-19,1991,77-84.
[27]陈秋晓．改进的RPCCL聚类方法及在遥感影像分割中的应用．计算机工程与应用，2005，34：221-223．
[28]黄利文，毛政元，李二振，等．基于几何概率的聚类分析方法及其在遥感影像分类中的应用．中国图象图形学报，2007，12(4)：633-640．
[29] Sang Ho Park, Il Dong Yun, Sang Uk Lee. Color image segmentation based on3-D clustering:Morphological approach. Pattern Recognition,1998,31:1061-1076.
[30] Kehtarnavaz N, Monaco J, Nimtschek J, et al. Color image segmentation using multi-scale clustering.Image5-7.Digital Object Identifier10.1109/IAI.1998.666875:142–147.
[31]林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法．中国图象图形学报．2004，9(2)：159-163．
[32] Yong Xia,(David) Dagan Feng, Tianjiao Wang, et al. Yanning Zhang. Image segmentation by clusteringof spatial patterns. Pattern Recognition,2007,28(12):1548-1555.
[33]毛政元，李霖．空间模式的测度及其应用．北京：科学出版社，2004．
[34] Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[35] Allen J T, Huntsberger T. Comparing color edge detection and segmentation methods. Southeastcon '89.Proceedings.'Energy and Information Technologies in the Southeast’. IEEE9-12, vol.2,April1989,722–728.
[35] Andreas Koschan. A Comparative Study On Color Edge Detection. Reprint from Proceedings2nd AsianConference on Computer Vision ACCV′95, Singapore,5-8December1995, Vol.III:574-578.
[37] Androutsos P, Androutsos D, Plataniotis K N, et al. Color edge detectors: an overview and comparison.Electrical and Computer Engineering,1997. IEEE1997Canadian Conference on Volume2,25-28, May1997,607-610.
[38] Ruzon M A, Tomasi C. Color edge detection with the compass operator. Computer Vision and PatternRecognition,1999. IEEE Computer Society Conference on,1999,2:23-25
[39] Koschan A, Abidi M. Detection and classification of edges in color images. Signal Processing Magazine.2005,22(1):64–73.
[40] Ying Tung Hsiao, Cheng Long Chuang, Joe Air Jiang, et al. A Contour based Image SegmentationAlgorithm using Morphological Edge Detection.Systems, Man and Cybernetics,2005IEEE InternationalConference on, Oct.2005,3(10-12):2962-2967.
[41]梅天灿，李德仁，秦前清．基于直线和区域特征的遥感影像线状目标检测．武汉大学学报﹒信息科学版．2005，30(8)：689-693．
[42] Nevada. A color edge detector and its use in scene segmentation. IEEE Trans. System ManCybernet.1977, SMC-7(11):820-826.
[43] P E Trahanias, A N Venetsanopoulos. Vector order statistics operators as color edge detectors. IEEETrans. Systems Man Cybernet.1996, Part B:Cybernetics,26(1):135-143.
[44] Bellon O R P, Silva L. New improvements to range image segmentation by edge detection. SignalProcessing Letters,2002,9(2):43-45.
[45] Lian Yishao, Bu Jiajun, Chen Chun. A novel image segmentation algorithm based on convex polygonedge detection.TENCON2004.2004IEEE Region10Conference on, Nov.2004, B(21-24):108-111.
[46] H D Cheng, Y Sun. A hierarchical approach to color image segmentation using homogeneity. IEEE Trans.Image Process.2000,9(12):2071-2082
[47] G M Espindola, G Camrar. Parameter selection for region-growing image segmentation algorithms usingspatial autocorrelation. International Journal of Remote Sensing,2006,27(14):3035–3040.
[48] Ikonomakis N, Pataniotis K N, Venetsanopoulos A N. Unsupervised seed determination for aregion-based color image segmentation scheme. Image Processing, Proceedings.2000InternationalConference on,2000,1(10-13):537–540.
[49] F Meyer, S Beucher. Morphology segmentation. J. Visual Comm. And Image Representation,1990,1(1):21-46.
[50] L Vincent, P Soille. Watershed in digital spaces: an efficient algorithm based on immersion simulations.IEEE Trans. Patt. Anal. And Mach.Int.1991,13(6):583-598.
[51] P De Smet, R L Pires. Implementation and analysis of an optimized rain falling watershed algorithm.Proc.SPIE,2000,3974:759-766.
[52] Yu Yuan, Barner K. Color Image Segmentation Using Watersheds and Joint Homogeneity-Edge IntegrityRegion Merging Criteria. Image. Oct.2006,8-11:1117–1120.
[53] R Ohlander, K Price, D R Reddy. Picture segmentation using a recursive region splitting method,Compute. Graphics Image Process,1978,8:313-333.
[54] Tremeau, N Borel. A region growing and merging algorithm to color segmentation. Pattern Recognition,1997,30(7):1191-1203.
[55]陈秋晓，陈述彭，周成虎．基于局域同质性梯度的遥感图像分割方法及其评价．遥感学报．2006，10(3)：357-365．
[56] Nayar S K, Ikeuchi K, Kanade T. Suface Reflection: Physical and Geometrical Perspectives. IEEE Trans.Pattern Analysis and Machine Intelligence,1991,13(7):611-634.
[57] Shafer S A. Using Color to Separate Reflection Components. Color Research and Application,1985,10:210-218.
[58] S A Shafer, T Kanade. Using shadows in finding surface orientations. Comput. Vision Graphics ImageProcess.1983,22:145-176.
[59] Lucchese L, Mitra S K. Advances in color image segmentation. Global Telecommunications Conference,1999.GLOBECOM '99,4:2038-2044.
[60] Bosworth J H, Acton S T. Morphological image segmentation by local monotonicity. Signals Systemsand Computers, Conference Record of the Thirty-Third Asilomar Conference on24-27Oct.1999,1:53–57.
[61]求是科技．Visual C++数字图像处理典型算法及实现．北京：人民邮电出版社，2006．
[62] T L Huntsberger, C L Jacobs, R L Cannon. Iterative fuzzy image segmentation. Pattern Recognition,1985,18(2):131-138.
[63] Caillol H, Hillion A, Pieczynski W. Fuzzy random fields and unsupervised image segmentation.Geoscience and Remote Sensing, IEEE Transactions on July1993,31(4):801–810.
[64] H D Cheng, J Li. Fuzzy homogeneity and scale space approach to color image segmentation. PatternRecognition, July2003,36(7):1545-1562.
[65] Pednekar A S, Kakadiaris I A. Image segmentation based on fuzzy connectedness using dynamic weights.Image Processing, IEEE Transactions on June2006,15(6):1555–1562.
[66] Borji A, Hamidi M. CLPSO-based Fuzzy Color Image Segmentation. North American FuzzyInformation Processing Society,2007. NAFIPS '07. Annual Meeting of the24-27June2007,508–513.
[67]崔锦泰[美]．小波分析导论．程正兴（译）．西安：西安交通大学出版社，1995．
[68] C S Lu, P C Chung, C F Chen. Unsupervised texture segmentation via wavelet transform. PatternRecognition,1997,30(5):729-742.
[69]张晓东，李德仁．atrous小波分解在边缘检测中的应用．武汉大学学报﹒信息科学版，2001，26(1)：29-33．
[70] Acharyya M, De R K, Kundu M K. Segmentation of remotely sensed images using wavelet features andtheir evaluation in soft computing framework. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2900–2905.
[71] M Egmont Petersena, D de Ridderb, H Handelsc. Image processing with neural networks—a review.Pattern Recognition,2002,35:2279–2301.
[72] Huang C L. Parallel image segmentation using modified Hopfield model. Pattern Recognition Letters,1993,13(5):345-353.
[73] Uscumlic Marija, Reljin Irini, Dujkovic Dragi. Improvements in Image Segmentation by ApplyingHopfield Neural Networks. Neural Network Applications in Electrical Engineering, NEUREL2006.8thSeminar on25-27Sept.2006,37–40.
[74] Y S Lo, S C Pei. Color image segmentation using local histogram and self-organization of Kohonenfeature map, International Conference on Image Processing, KOBE, Japan, October24-28,1999,232-239.
[75] Muhammed H H. Unsupervised fuzzy clustering and image segmentation using weighted neuralnetworks. Image Analysis and Processing.Proceedings.12th International Conference on17-19Sept.2003,308-313.
[76]刘永学，李满春．基于边缘的多光谱遥感图像分割方法．遥感学报，2006，10(3)：350-356．
[77] X Mu oz, J Freixenet, X Cufí, et al. Strategies for image segmentation combining region and boundaryinformation. Pattern Recognition Letters,2003,24:375-392.
[78] eCognition User Guide[EB/OL],2002,http:\\www.definiens-imaging.com.
[79] Yining Deng, B S Manjunath. Unsupervised Segmentation of Color-Texture Regions in Images andVideo. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(8):800-810.
[80] Jing F, Li M, Zhang H, et al. Unsupervised Image Segmentation Using Local HomogeneityAnalysis.Proc.IEEE International Symposium on Circuits and Systems.2003,456-459.
[81] Zadeh L A. Fuzzy sets. Information and Control,1965,8(3):338-353.
[82]刘建华．顾及分割效果的QuickBird影像融合方法对比研究．国土资源遥感，2010，3：21-25．
[83]刘建华．多色彩高空间分辨率遥感影像矢量边缘信息提取算法与应用．地球信息科学学报，2010，12(6)：850-854．
[84] Liu Jianhua, Mao Zhengyuan.Vector and Scalar Edge Extraction from Multi-Spectral High SpatialResolution Remotely Sensed Imagery (MHSRRSI) in Weighted Color Space. IEEE International Conferenceon Multimedia Information Networking and Security,2010:932-935.
[85]胡钢，刘哲，徐小平，等．像素级图像融合技术的研究与进展．计算机应用研究，2008，25(3)：650-655．
[86]刘松涛，周晓东．图像融合技术研究的最新进展．激光与红外，2006，36(8)：627-631．
[87] Schowengerdt R A. Reconstruction of Multispatial, Multispectral Image Data Using Spatial FrequencyContent. Photogrammetric Engineering and Remote Sensing,1980,46(10):1325-1334.
[88] Liu J G. Evaluation of Landsat-7ETM+Panchromatic Band for Image Fusion with Multispectral Bands.Natural Resource Research,2000,9(4):269-276.
[89] Clayton D G. Gram-Schmidt Orthogonalization. Applied Statistics,1971,20(3):335-338.
[90] Vrabel J, Doraiswamy P, McMurtrey J, et al. Demonstration of the Accuracy of Improved ResolutionHyperspectral Imagery. Hutchison:D., Katz, R.H.(eds.) IWSOS,2007,556-567.
[91] Ehlers M. Spectral Characteristics Preserving Image Fusion Based on Fourier Domain Filtering. Proc.SPIE,2004,5574:1-13.
[92] Klonus S, Ehlers M. Image Fusion Using the Ehlers Spectral Characteristics Preserving Algorithm.GIScience&Rem. Sens.,2007,44:93–116.
[93] Liu J G. Smoothing Filter-based Intensity Modulation: A Spectral Preserve Image Fusion Technique forImproving Spatial Details. International Journal of Remote Sensing,2000,21(18):3461-3472.
[94]李存军，刘良云．两种高保真遥感影像融合方法比较．中国图象图形学报，2004，9(11)：1376-1385．
[95] D D Lee, H S Seung. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature,1999,401(6755):788-791.
[96] D D Lee, H S Seung. Algorithms for Non-negative Matrix Factorization,2000,http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf
[97] Do M N, Vetterli M. The Contourlet Transform: An Efficient Diectional Multiresolution ImageRepresentation. IEEE Trans. Image Processing,2005,14(12):2091-2106.
[98] Miao Qiguang, Wang Baoshu. A Novel Image Fusion Method Using Contourlet Transform.4thInternational Conference on Communications, Circuits and Systems, ICCCAS,2006.
[99] Zhijun Wang, Djemel Ziou, Costas Armenakis, et al. A Comparative Analysis of Image FusionMethods. IEEE Trans. Geoscience and Remote Sensing,2005,43(6):1391-1402.
[100] Pouran Behnia. Comparison between Four Methods for Data Fusion of ETM+Multispectral and PanImages. Geo-spatial Information Science (Quarterly).2005,8(2):98-103.
[101]郭雷，李晖晖．图像融合．北京：电子工业出版社，2008．
[102] J Astola, P Haavisto, Y Neuvo.Vector median filters. Proc. IEEE, Apr.1990,78:678–89.
[103] L Yin, R Yang, M Gabbouj, et al.Weighted median filters:A tutorial. IEEE Trans. Circuits Syst. II,1996,43:157–192.
[104] H G Longbotham, D Eberly. Statistical properties, fixed points, and decomposition with WMMR filters.J. Math. Imag. Vis.,1992,2:99–116.
[105] H G Longbotham, D Eberly. WMMR filters: A class of robust edge enhancers. IEEE Trans. SignalProcessing,1992,41:1680–1684.
[106] L J Christiano, T J Fitzgerald.The Band Pass Filter. International Economic Review,2003,44:435–465.
[107] G Hewer, C Kenney, B S Manjunath. Peer group processing for segmentation.Technical Report#97-01, ECE Dept., UCSB,1997.
[108] Y Deng, C Kenney, M S Moore, et al. Peer group filtering and perceptual color image quantization. inProc. IEEE Int. Symp. Circuits and Systems,1999,4:21–24.
[109] Y Deng, G Hewer, C Kenney, et al. Peer group image processing. Univ. Calif., SantaBarbara, ECE Tech. Rep., Nov.1999.
[110] G Hewer, C Kenney, B Manjunath. Variational image segmentation using boundary functions.IEEE Trans. Image Processing, Sept.1998,7:1269–1282,.
[111] J Morel, S Solimini. Variational Methods in Image Segmentation. Boston, MA: Birkhauser,1995.
[112] D Mumford, J Shah. Boundary detection by minimizing functionals. in IEEE Conf. ComputerVision Pattern Recognition, San Francisco, CA,1985,22–26.
[113] D Mumford, J Shah. Optimal approximation by piecewise smooth functions and associated variationalproblems.Commun. Pure Appl. Math.,1989.
[114] M Oman. Study of variational methods applied to noisy step data.1996.
[115] S Osher, L Rudin. Feature-oriented image enhancement using shock filters.SIAM J. Numer. Anal.,1990,27:919–940.
[116] S Osher, L Rudin. Shocks and other nonlinear filtering applied to image processing.in Proc. SPIE Appl.Dig. Image Proc. XIV,1991,1567:414–430.
[117] L Rudin. Images, numerical analysis of singularities, and shock filters. Ph.D. dissertation, ComputerScience Dept., Caltech, Pasadena, CA,1987.
[118] C Tomasi, R Manduchi. Bilateral Filtering for Gray and Color Images. Proceedings of the1998IEEEInternational Conference on Computer Vision, Bombay, India,1998.
[119] Michael Elad. On the Origin of the Bilateral Filter and Ways to Improve It. IEEE Transactions onImage Processing,2002,11(10):1141-1151,.
[120] P Perona, J Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEETransactions on Pattern Analysis and Machine Intelligence,1990,12(7):629-631.
[121] G Sapiro, D L Ringach. Anisotropic diffusion of color images. Proc. SPIE,1996,471:382–2657.
[122] Black M J, Sapiro G, Marimont D, et al. Robust Anisotropic Diffusion.IEEE Transactions onImage Processing,1998,7(3):421-426.
[123] J Weickert. Anisotropic Diffusion in Image Processing, Tuebner Stuttgart,1998. ISBN3-519-02606-6.
[124] Z Lin, Q Sh. An anisotropic diffusion PDE for noise reduction and thin edge preservation.in Proc.10thInt. Conf. Image Analysis and Processing, IEEE Computer Society, Los Alamitos, CA,1999,102–107.
[125]周光绍，徐勇．在高分辨率遥感影像中提取无清晰连续边缘线的道路．测绘学报，2008，37(3)：301-307．
[126]梅天灿，李德仁，秦前清．基于直线和区域特征的遥感影像线状目标检测．武汉大学学报﹒信息科学版，2005，30(8)：689-693．
[127]程承旗，马廷．高分辨率卫星影像上地物线性特征的自动识别．遥感学报，2003，7(1)：26-30．
[128]明冬萍，骆剑承，周成虎．高分辨率遥感影像特征分割及算法评价分析．地球信息科学，2006，8(1)：103-109．
[129]骆剑承，周成虎，沈占锋，等．遥感信息图谱计算的理论方法研究．地球信息科学学报，2009，11(5)：664-669．
[130] Andreas Koschan, Mongi Abidi.彩色数字图像处理．章毓晋(译)．北京：清华大学出版社，2010．
[131]王志衡，吴福朝．内积能量与边缘检测．计算机学报，2009，32（11）：2211-2221．
[132]林生佑，石教英．基于HSV的彩色图像边缘检测算子．中国图象图形学报，2005，10(1)：43-47．
[133]王小芳，闫光荣，雷毅．彩色图像的复变函数模型及边缘检测．光电工程，2008，35(2)：90-96．
[134]解振东．基于Canny理论的彩色图像边缘检测．物探化探计算技术，2007，29(4)：370-373．
[135]高俊钗，韩冰，王泽民．向量空间彩色图像的Canny边缘检测．科学技术与工程，2008，8(3)：686-689．
[136] Yang C K, Tsai W H. Reduction of color space dimensionality by moment preserving thresholding andits application for edge detection in color images. Pattern Recognition Letters,1996,17(5):481-490.
[137] Wesolkowski S, Jernigan M E, D D Robert. Comparison of color image edge detectors in multiple colorspaces. Proceedings of the International Conference on Image Processing. Vancouver, BC, Canada: IEEE,2000,2:796-799.
[138]王植，贺赛先．一种基于Canny理论的自适应边缘检测方法．中国图象图形学报，2004，9(8)：957-962．
[139] Li Jun ding, Ardeshir Goshtasby. On the Canny edge detector.Pattern Recognition,2001,34:721-725.
[140] Ehlers M. Spectral Characteristics Preserving Image Fusion Based on Fourier Domain Filtering.Proc. SPIE,2004,5574:1-13.
[141] J R Beveridge, J K Griffith, E M Riesman. Segmenting images using localized histograms and regionmerging. International Journal of Computer Vision,1989,2:311–347.
[142] R Adams, L Bischof. Seeded region growing. IEEE Transactions on Pattern Analysis and MachineIntelligence,1994,16(6):641-647.
[143] A Mehnert, P Jackway. An improved seeded region growing algorithm. Pattern Recognition Letters,1997,18(10):1065-1071.
[144] K Haris, S N Efstratiadis, N Maglaveras, et al. Hybrid image segmentation using watersheds and fastregion merging. IEEE Trans. on Image Processing,1998,7(12):1684-1699.
[145] K Fukunaga, L D Hostetler. The Estimation of the Gradient of a Density Function, withApplications in Pattern Recognition. IEEE Trans. on Information Theory,1975,21(1):32-40.
[146] Yizong Cheng. Mean shift, mode seeking, and clustering.IEEE Trans. on Pattern Analysis and MachineIntelligence,1995,17(8):790-799.
[147] D Comaniciu, V Ramesh, P Meer. Real-time tracking of non-rigid objects using mean shift.Proc. IEEEConfe. Computer Vision and Pattern Recognition,2000,2:142-149.
[148] D Comaniciu,V Ramesh, P Meer.The VariableBandwidth Mean Shift and Data-Driven Scale Selection.International Conference on Computer Vision,2001,1:438-445.
[149] D Comaniciu, P Meer. Mean Shift: a robust approach toward feature space analysis. IEEE Trans. onPattern Analysis and Machine Intelligence,2002,24(5):603-619.
[150] D Comaniciu. An algorithm for data-driven bandwidth selection. IEEE Trans. on Pattern Analysis andMachine Intelligence,2003,25(2):281-288.
[151] Falah R, Bolon P, Cocquerez J. A region-region and region-edge cooperative approach of imagesegmentation. In: International Conference on Image Processing, October,Vol.3. Austin, Texas,1994,470–474.
[152] T Pavlidis, Y T Liow. Integrating region growing and edge detection. IEEE Trans. Pattern Anal.Machine Intell.,1990,121:225-233.
[153] J F Haddon, J F Boyce. Image segmentation by unifying region and boundary information. IEEE Trans.Pattern Anal. Machine Intell.,1990,12:929-948.
[154] C C Chu, J K Agganval. The integration of image segmentation maps using region and edgeinformation. IEEE Trans. Pattern Anal. Machine Intell.,1993,15:1241-1252.
[155] J Le Moigne, J C Tilton. Refining image segmentation by integration of edge and region data. IEEETrans. Geoscience and Remote Sensing,1995,33:605-615.
[156] M Tabb, N Ahuja. Multiscale image segmentation by integrated edge and region detection. IEEETrans. Image Process.,1997,6:642-655.
[157] W Y Ma, B S Manjunath. Edge flow: A framework of boundary detection and image segmentation.IEEE Trans.Image Processing,2000,9:1375-1388.
[158] J Fan, D K Y Yau, A K Elmagarmid. Automatic image segmentation by integrating color-edgeextraction and seeded region growing. IEEE Trans. Image Process.,2001,10:1454-1466.
[159] Fukada Y. Spatial clustering procedures for region analysis. Pattern Recognition,1980,12:395-403.
[160] Chen P, Pavlidis T. Image segmentation as an estimation problem. Computer Graphics and ImageProcessing,1980,12:153-172.
[161] Bonnin P, Blanc Talon J. A new edge point/region cooperative segmentation deduced from a3d scenereconstruction application. In: SPIE Applications of Digital Image Processing XII,1989,1153:579-591.
[162] Buvry M, Senard J, Krey C. Hierarchical region detection based on the gradient image. In:Scandinavian Conference on Image Analysis, June, Lappeenranta, Finland,1997,2:717-724.
[163] Buvry M, Zagrouba E, Krey C. A rule-based system for region segmentation improvement instereovision. In:Society of Photo-Optical Instrumentation Engineers, February, Image and Video ProcessingII. San Jose,California,1994,2182:357-367.
[164] Healey G. Segmenting images using normalized color.IEEE Transactions on Systems, Man, andCybernetics,1992,22(1):64-73.
[165] Bertolino P, Montanvert A. Cooperation regions contours multiresolution en segmentation d’image.In:Congres AFCET de Reconnaissance des Formes et Intelligence Artificielle, January, Vol.1. Rennes,France,1996,299-307.
[166] Gevers T, Smeulders A. Combining region growing and edge detection through guided delaunay imagesubdivision.In: IEEE Computer Society Conference on ComputerVision and Pattern Recognition, June,Puerto Rico,1997,1021-1026.
[167] Zucker S. Region growing: Childhood and adolescence.Computer Graphics and Image Processing,1976,5:382-399.
[168] Zucker S. Algorithms for image segmentation. In:Digital Image Processing and Analysis,1977,169-183.
[169] Adams R, Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and MachineIntelligence,1994,16(6):641–647.
[170] Xiaohan Y, Yla Jaaski J. Image segmentation combiningregion growing and edge detection. In:International Conference on Pattern Recognition, August, Vol. C. The Hague,Netherlands,1992,481–484.
[171] Gambotto J. A new approach to combining region growing and edge detection. Pattern RecognitionLetters,1993,14:869–875.
[172] Steudel A, Glesner M. Fuzzy segmented image coding using orthonormal bases and derivative chaincoding.Pattern Recognition,1999,32(11):1827–1841.
[173] Krishnan S, Tan C, Chan K. Closed-boundary extraction of large intestinal lumen. In: InternationalConference of the IEEE Engineering in Medicine and Biology Society, November, Baltimore, Washington,1994.
[174] Lambert P, Carron T. Symbolic fusion of luminancehue-croma features for region segmentation. PatternRecognition,1999,32(11):1857–1872.
[175] Pham D, Prince J. An adaptive fuzzy c-means algorithm for image segmentation in the presence ofintensity inhomogeneities. Pattern Recognition Letters,1999,20(1):57–68.
[176] Moghaddamzadeh A, Bourbakis N. A fuzzy region growing approach for segmentation of color images.Pattern Recognition,1997,30(6):867–881.
[177] Kong S, Kosko B. Image coding with fuzzy image segmentation. In: International Conference on FuzzySystems, March, Vol. I. San Diego, California,1992,213–220.
[178] Bieniek A, Moga A. An efficient watershed algorithm based on connected components. PatternRecognition,2000,33(6):907–916.
[179] De Smet P, Pires R, Vleeschauwer D, et al. Implementation and analysis of an optimized rainfallingwatershed algorithm. Journal of Electronic Imaging,1999,8(3):270–278.
[180] Wang D. A multiscale gradient algorithm for image segmentation using watersheds. PatternRecognition,1997,30(12):2043–2052.
[181] Weickert J. Efficient image segmentation using partial differential equations and morphology. PatternRecognition,2001,34(9):1813–1824.
[182] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. In: International Conference onComputer Vision, June, London, UK,1987,259–268.
[183] Alexander D, Buxton B. Implementational improvements for active region models. In: British MachineVision Conference. Colchester, UK,1997.
[184] Chakraborty A, Staib L, Duncan J. Deformable boundary finding influenced by region homogeneity.In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June, Vol.94. Seattle,Washington,1994,624–627.
[185] Chakrabory A, Duncan J. Game-theoretic integration for image segmentation. IEEE Transactions onPattern Analysis and Machine Intelligence,1999,21(1):12–30.
[186] Mumford D, Shah J. Boundary detection by minimizing functionals. In: IEEE Computer SocietyConference on Computer Vision and Pattern Recognition. San Francisco, California,1985,85:22-26.
[187] Shah J, Pien H, Gauch J. Recovery of surfaces with discontinuities by fusing shading and range datawithin a variational framework. IEEE Transactions on Image Processing,1996,5(8):1243–1251.
[188] Zhu S, Yuille A. Region competition: Unifying snakes, region growing, and bayes/mdl for multi-bandimage segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(9):884–900.
[189] Ronfard R. Region-based strategies for active contours models. International Journal of ComputerVision,1994,13(2):229–251.
[190] Ivins J. Statistical snakes: Active region models. Ph.D.thesis, University of Sheffield,1996.
[191] Ivins J, Porrill J. Active-region models for segmenting textures and colors. Image and VisionComputing,1995,13(5):431–438.
[192] Chesnaud C, Rfrgier P, Boulet V. Statistical region snake-based segmentation adapted to differentphysical noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(11):1145-1157.
[193] Paragios N, Deriche R. Geodesic active regions for supervised texture segmentation. In: InternationalConference on Computer Vision, September, Vol. II. Corfu, Greece,1999,926–932.
[194] Paragios N, Deriche R. Unifying boundary and region-based information for geodesic active tracking.In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June, Vol. II. FortCollins,Colorado,1999,422–427.
[195] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. International Journal of Computer Vision,1997,22(1):61–79.
[196] Will S, Hermes L, Buhmann J, et al. On learning texture edge detectors. In: International Conference onImage Processing, September, Vol. III. Vancouver, Canada,2000,887-880.
[197] Sinclair D. Voronoi seeded colour image segmentation.Tech. Rep.3, AT&T Laboratories Cambridge,1999.
[198] Cufi X, Munoz X, Freixenet J, et al. A concurrent region growing algorithm guided by circumscribedcontours. In: International Conference on Pattern Recognition, September, Vol. I. Barcelona, Spain,2000,432-435.
[199] Gagalowicz A, Monga O. A new approach for image segmentation. In: International Conference onPattern Recognition, November, Paris, France,1986,265-267.
[200] B Wrobel, O Monga. Segmentation d'images naturelles: coopération entre un détecteur contour et undétecteur région. In Actes du Onzième colloque GRETSI, Nice, France, Juin1987.
[201] Philipp S, Zamperoni P. Segmentation and contour closing of textured and non-textured images usingdistances between textures. In: International Conference on Image Processing, September, Vol. C. Lausanne,Switzerland,1996,125-128.
[202] FjOrtoft R, Cabada J, Lopes A, et al. Complementary edge detection and region growing for sar imagesegmentation. In: Conference of the Norwegian Society for Image Processing and Pattern Recognition, MayVol.1. TromsO, Norway,1997,70-72.
[203] Haddon J, Boyce J. Image segmentation by unifying region and boundary information. IEEETransactions on Pattern Analysis and Machine Intelligence,1990,12(10):929-948.
[204] Chu C, Aggarwal J. The integration of image segmentation maps using region and edge information.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(12):1241-1252.
[205] Nair D, Aggarwal J. A focused target segmentation paradigm. In: European Conference on ComputerVision, April, Vol. A. Cambridge, UK,1996,579-588.
[206] Sato M, Lakare S, Wan M, et al. A gradient magnitude based region growing algorithm for accuratesegmentation. In: International Conference on Image Processing, September, Vol. III. Vancouver, Canada,2000,448-451.
[207] Bergholm F. Edge focusing. IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(6):726-741.
[208] Spann M, Wilson R. A quad-tree approach to image segmentation which combines statistical andspatial information.Pattern Recognition,1985,18(3/4):257-269.
[209] Spann M, Wilson R. A new approach to clustering.Pattern Recognition,1990,23(12):1413-1425.
[210] Wilson R, Spann M. Finite prolate spheroidial sequences and their applications. II. Image featuredescription and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(2):193-203.
[211] Hsu T, Kuo J, Wilson R. A multiresolution texture gradient method for unsupervised segmentation.Pattern Recognition,2000,32(11):1819-1833.
[212] Chan F, Lam F, Poon P, et al. Object boundary location by region and contour deformation.IEEEProceedings––Vision Image and Signal Processing,1996,143(6):353-360.
[213] Verard L, Fadili J, Ruan S, et al.3d mrisegmentation of brain structures. In: International Conference ofthe IEEE Engineering in Medicine and Biology Society, November, Amsterdam, Netherlands,1996,1081-1082.
[214] Jang D, Lee D, Kim S. Contour detection of hippocampus using dynamic contour model and regiongrowing. In: International Conference of the IEEE Engineering in Medicine and Biology Society, October,Chicago,Ilinois,1997,763–766.
[215] Fua P, Hanson A. Using generic geometric models for intelligent shape extraction. In: NationalConference on Artificial Intelligence, July, Seattle, Washington,1987,706-711.
[216] Lemoigne J, Tilton J. Refining image segmentation by integration of edge and region data. IEEETransactions on Geoscience and Remote Sensing,1995,33(3),605-615.
[217] Hojjatoleslami S, Kittler J. Region growing: A new approach. IEEE Transactions on Image Processing,1998,7(7):1079-1084.
[218] Siebert A. Dynamic region growing. In: Vision Interface,May, Kelowna, Canada,1997.
[219] Revol Muller C, Peyrin F, Odet C, et al. Automated3d region growing algorithm governed by anevaluation function. In: International Conference on Image Processing, September, Vol. III. Vancouver,Canada,2000,440-443.
[220]胡晓东，骆剑承，沈占锋，等．高分辨率遥感影像并行分割结果缝合算法．遥感学报，2010，14(5)：917-927．
[2]陈秋晓．高分辨率遥感影像分割方法研究：［博士学位论文］．北京：中国科学院研究生院，2004．
[3]刘建华，毛政元．高空间分辨率光学遥感影像分割方法研究综述．遥感信息，2009，6：95-101．
[4] T Blaschke, J Strobl. What’s wrong with pixels? Some recent developments interfacing remote sensingand GIS. GeoBIT/GIS:2001,12-17.
[5] A Lobo, O Chic, A Casterad. Classification of Mediterranean Crops with Multisensor Data: Per-Pixelversus Per-Object Statistics and Image Segmentation. International Journal of Remote Sensing.1996,17:2358-2400.
[6] T Blaschke, S Lang, E Lorup, et al. Object-oriented image processing in an integrated GIS/remote sensingenvironment and perspectives for environmental applications. Environmental Information for Planning,2000,2:555-570.
[7] M Baatz, A Sch pe. Multiresolution Segmentation–an Optimization Approach for High QualityMulti-scale Image Segmentation. In: STROBL, J. et al.(Hrsg.): Angewandte GeographischeInformationsverarbeitung XII. Beitr ge zum AGIT-Symposium Salzburg2000, Karlsruhe, Herbert WichmannVerlag:12–23.
[8] J Schiewe, L Tufte, M Ehlers. Potential and problems of multi-scale segmentation methods in remotesensing.GIS–Zeitschrift für Geoinformations system,2001,(6):34-39.
[9] S Giada, T D Groeve, D Ehrlich. Information extraction from very high resolution satellite imagery overLukole refugee camp, Tanzania. International Journal of Remote Sensing,2003,24(22):4251–4266.
[10] Wang Z, Wei W, Zhao S. Object-oriented classification and application in land use classification usingSPOT-5PAN imagery. In: International Geoscience and Remote Sensing Symposium (IGARSS), v5,2004:3158-3160.
[11] Hurd J D, Civco D L, Gilmore M S. Tidal Wetland Classification from Landsat Imagery Using anIntegrated Pixel-Based and Object-Based Classification Approach. ASPRS2006Annual Conference, Reno,Nevada,2006.
[12]章毓晋．图象分割．北京:科学出版社，2001，36-58．
[13]蔡银桥，毛政元．基于多特征对象的高分辨率遥感影像分类方法及其应用．国土资源感，2007，(9)：77-81．
[14]宫鹏，黎夏，徐冰．高分辨率影像解译理论与应用方法中的一些研究问题．遥感学报，2006，10(1)：1-5．
[15] K S Fu, J K Mui. A survey on image segmentation. Pattern Recognition,1981,13:3-16.
[16] S K Pal. A review on image segmentation techniques. Pattern Recognition29(1993):1277-1294.
[17] Wladyslaw Skarbek, Andreas Koschan. Colour Image Segmentation-A Survey. October1994.http://imaging.utk.edu/~koschan/paper/coseg.pdf.
[18]罗希平，田捷，诸葛婴，等．图像分割方法综述．模式识别与人工智能，1999，12(3)：300-312．
[19]王爱民，沈兰荪．图像分割研究综述．测控技术，2000，19(5)：20-24．
[20] H D Cheng, X H Jiang, Y Sun, et al. Color image segmentation: advances and prospects. PatternRecognition,2001,34(12):2259-2281.
[21]林瑶，田捷．医学图像分割方法综述．模式识别与人工智能，2002，15(2)：192-204．
[22]林开颜，吴军，徐立鸿．彩色图像分割方法综述．中国图象图形学报．2005，10(1)：1-10．
[23] Yu Jin Zhang. Advances in Image and Video Segmentation: An Overview of Image and VideoSegmentation in the Last40Years.2006.http://www.ee.tsinghua.edu.cn/~zhangyujin/3_5.htm
[24] P K Sahoo, S Soltani, A K C Wong, et al. A survey of thresholding techniques. Computer Vision,Graphics, and Image Processing,1988,41(2):233-260.
[25] Mehmet Sezgin, Bulent Sankur.Survey over image thresholding techniques and quantitative performanceevaluation. Journal of Electronic Imaging,2004,13(1):146–165.
[26] Bonsiepen L, Coy W. Stable segmentation using color information. In Computer Analysis of Images andPatterns, ed. R. Klette, Proc.of CAIP'91, Dresden, September17-19,1991,77-84.
[27]陈秋晓．改进的RPCCL聚类方法及在遥感影像分割中的应用．计算机工程与应用，2005，34：221-223．
[28]黄利文，毛政元，李二振，等．基于几何概率的聚类分析方法及其在遥感影像分类中的应用．中国图象图形学报，2007，12(4)：633-640．
[29] Sang Ho Park, Il Dong Yun, Sang Uk Lee. Color image segmentation based on3-D clustering:Morphological approach. Pattern Recognition,1998,31:1061-1076.
[30] Kehtarnavaz N, Monaco J, Nimtschek J, et al. Color image segmentation using multi-scale clustering.Image5-7.Digital Object Identifier10.1109/IAI.1998.666875:142–147.
[31]林开颜，徐立鸿，吴军辉．快速模糊C均值聚类彩色图像分割方法．中国图象图形学报．2004，9(2)：159-163．
[32] Yong Xia,(David) Dagan Feng, Tianjiao Wang, et al. Yanning Zhang. Image segmentation by clusteringof spatial patterns. Pattern Recognition,2007,28(12):1548-1555.
[33]毛政元，李霖．空间模式的测度及其应用．北京：科学出版社，2004．
[34] Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[35] Allen J T, Huntsberger T. Comparing color edge detection and segmentation methods. Southeastcon '89.Proceedings.'Energy and Information Technologies in the Southeast’. IEEE9-12, vol.2,April1989,722–728.
[35] Andreas Koschan. A Comparative Study On Color Edge Detection. Reprint from Proceedings2nd AsianConference on Computer Vision ACCV′95, Singapore,5-8December1995, Vol.III:574-578.
[37] Androutsos P, Androutsos D, Plataniotis K N, et al. Color edge detectors: an overview and comparison.Electrical and Computer Engineering,1997. IEEE1997Canadian Conference on Volume2,25-28, May1997,607-610.
[38] Ruzon M A, Tomasi C. Color edge detection with the compass operator. Computer Vision and PatternRecognition,1999. IEEE Computer Society Conference on,1999,2:23-25
[39] Koschan A, Abidi M. Detection and classification of edges in color images. Signal Processing Magazine.2005,22(1):64–73.
[40] Ying Tung Hsiao, Cheng Long Chuang, Joe Air Jiang, et al. A Contour based Image SegmentationAlgorithm using Morphological Edge Detection.Systems, Man and Cybernetics,2005IEEE InternationalConference on, Oct.2005,3(10-12):2962-2967.
[41]梅天灿，李德仁，秦前清．基于直线和区域特征的遥感影像线状目标检测．武汉大学学报﹒信息科学版．2005，30(8)：689-693．
[42] Nevada. A color edge detector and its use in scene segmentation. IEEE Trans. System ManCybernet.1977, SMC-7(11):820-826.
[43] P E Trahanias, A N Venetsanopoulos. Vector order statistics operators as color edge detectors. IEEETrans. Systems Man Cybernet.1996, Part B:Cybernetics,26(1):135-143.
[44] Bellon O R P, Silva L. New improvements to range image segmentation by edge detection. SignalProcessing Letters,2002,9(2):43-45.
[45] Lian Yishao, Bu Jiajun, Chen Chun. A novel image segmentation algorithm based on convex polygonedge detection.TENCON2004.2004IEEE Region10Conference on, Nov.2004, B(21-24):108-111.
[46] H D Cheng, Y Sun. A hierarchical approach to color image segmentation using homogeneity. IEEE Trans.Image Process.2000,9(12):2071-2082
[47] G M Espindola, G Camrar. Parameter selection for region-growing image segmentation algorithms usingspatial autocorrelation. International Journal of Remote Sensing,2006,27(14):3035–3040.
[48] Ikonomakis N, Pataniotis K N, Venetsanopoulos A N. Unsupervised seed determination for aregion-based color image segmentation scheme. Image Processing, Proceedings.2000InternationalConference on,2000,1(10-13):537–540.
[49] F Meyer, S Beucher. Morphology segmentation. J. Visual Comm. And Image Representation,1990,1(1):21-46.
[50] L Vincent, P Soille. Watershed in digital spaces: an efficient algorithm based on immersion simulations.IEEE Trans. Patt. Anal. And Mach.Int.1991,13(6):583-598.
[51] P De Smet, R L Pires. Implementation and analysis of an optimized rain falling watershed algorithm.Proc.SPIE,2000,3974:759-766.
[52] Yu Yuan, Barner K. Color Image Segmentation Using Watersheds and Joint Homogeneity-Edge IntegrityRegion Merging Criteria. Image. Oct.2006,8-11:1117–1120.
[53] R Ohlander, K Price, D R Reddy. Picture segmentation using a recursive region splitting method,Compute. Graphics Image Process,1978,8:313-333.
[54] Tremeau, N Borel. A region growing and merging algorithm to color segmentation. Pattern Recognition,1997,30(7):1191-1203.
[55]陈秋晓，陈述彭，周成虎．基于局域同质性梯度的遥感图像分割方法及其评价．遥感学报．2006，10(3)：357-365．
[56] Nayar S K, Ikeuchi K, Kanade T. Suface Reflection: Physical and Geometrical Perspectives. IEEE Trans.Pattern Analysis and Machine Intelligence,1991,13(7):611-634.
[57] Shafer S A. Using Color to Separate Reflection Components. Color Research and Application,1985,10:210-218.
[58] S A Shafer, T Kanade. Using shadows in finding surface orientations. Comput. Vision Graphics ImageProcess.1983,22:145-176.
[59] Lucchese L, Mitra S K. Advances in color image segmentation. Global Telecommunications Conference,1999.GLOBECOM '99,4:2038-2044.
[60] Bosworth J H, Acton S T. Morphological image segmentation by local monotonicity. Signals Systemsand Computers, Conference Record of the Thirty-Third Asilomar Conference on24-27Oct.1999,1:53–57.
[61]求是科技．Visual C++数字图像处理典型算法及实现．北京：人民邮电出版社，2006．
[62] T L Huntsberger, C L Jacobs, R L Cannon. Iterative fuzzy image segmentation. Pattern Recognition,1985,18(2):131-138.
[63] Caillol H, Hillion A, Pieczynski W. Fuzzy random fields and unsupervised image segmentation.Geoscience and Remote Sensing, IEEE Transactions on July1993,31(4):801–810.
[64] H D Cheng, J Li. Fuzzy homogeneity and scale space approach to color image segmentation. PatternRecognition, July2003,36(7):1545-1562.
[65] Pednekar A S, Kakadiaris I A. Image segmentation based on fuzzy connectedness using dynamic weights.Image Processing, IEEE Transactions on June2006,15(6):1555–1562.
[66] Borji A, Hamidi M. CLPSO-based Fuzzy Color Image Segmentation. North American FuzzyInformation Processing Society,2007. NAFIPS '07. Annual Meeting of the24-27June2007,508–513.
[67]崔锦泰[美]．小波分析导论．程正兴（译）．西安：西安交通大学出版社，1995．
[68] C S Lu, P C Chung, C F Chen. Unsupervised texture segmentation via wavelet transform. PatternRecognition,1997,30(5):729-742.
[69]张晓东，李德仁．atrous小波分解在边缘检测中的应用．武汉大学学报﹒信息科学版，2001，26(1)：29-33．
[70] Acharyya M, De R K, Kundu M K. Segmentation of remotely sensed images using wavelet features andtheir evaluation in soft computing framework. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2900–2905.
[71] M Egmont Petersena, D de Ridderb, H Handelsc. Image processing with neural networks—a review.Pattern Recognition,2002,35:2279–2301.
[72] Huang C L. Parallel image segmentation using modified Hopfield model. Pattern Recognition Letters,1993,13(5):345-353.
[73] Uscumlic Marija, Reljin Irini, Dujkovic Dragi. Improvements in Image Segmentation by ApplyingHopfield Neural Networks. Neural Network Applications in Electrical Engineering, NEUREL2006.8thSeminar on25-27Sept.2006,37–40.
[74] Y S Lo, S C Pei. Color image segmentation using local histogram and self-organization of Kohonenfeature map, International Conference on Image Processing, KOBE, Japan, October24-28,1999,232-239.
[75] Muhammed H H. Unsupervised fuzzy clustering and image segmentation using weighted neuralnetworks. Image Analysis and Processing.Proceedings.12th International Conference on17-19Sept.2003,308-313.
[76]刘永学，李满春．基于边缘的多光谱遥感图像分割方法．遥感学报，2006，10(3)：350-356．
[77] X Mu oz, J Freixenet, X Cufí, et al. Strategies for image segmentation combining region and boundaryinformation. Pattern Recognition Letters,2003,24:375-392.
[78] eCognition User Guide[EB/OL],2002,http:\\www.definiens-imaging.com.
[79] Yining Deng, B S Manjunath. Unsupervised Segmentation of Color-Texture Regions in Images andVideo. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(8):800-810.
[80] Jing F, Li M, Zhang H, et al. Unsupervised Image Segmentation Using Local HomogeneityAnalysis.Proc.IEEE International Symposium on Circuits and Systems.2003,456-459.
[81] Zadeh L A. Fuzzy sets. Information and Control,1965,8(3):338-353.
[82]刘建华．顾及分割效果的QuickBird影像融合方法对比研究．国土资源遥感，2010，3：21-25．
[83]刘建华．多色彩高空间分辨率遥感影像矢量边缘信息提取算法与应用．地球信息科学学报，2010，12(6)：850-854．
[84] Liu Jianhua, Mao Zhengyuan.Vector and Scalar Edge Extraction from Multi-Spectral High SpatialResolution Remotely Sensed Imagery (MHSRRSI) in Weighted Color Space. IEEE International Conferenceon Multimedia Information Networking and Security,2010:932-935.
[85]胡钢，刘哲，徐小平，等．像素级图像融合技术的研究与进展．计算机应用研究，2008，25(3)：650-655．
[86]刘松涛，周晓东．图像融合技术研究的最新进展．激光与红外，2006，36(8)：627-631．
[87] Schowengerdt R A. Reconstruction of Multispatial, Multispectral Image Data Using Spatial FrequencyContent. Photogrammetric Engineering and Remote Sensing,1980,46(10):1325-1334.
[88] Liu J G. Evaluation of Landsat-7ETM+Panchromatic Band for Image Fusion with Multispectral Bands.Natural Resource Research,2000,9(4):269-276.
[89] Clayton D G. Gram-Schmidt Orthogonalization. Applied Statistics,1971,20(3):335-338.
[90] Vrabel J, Doraiswamy P, McMurtrey J, et al. Demonstration of the Accuracy of Improved ResolutionHyperspectral Imagery. Hutchison:D., Katz, R.H.(eds.) IWSOS,2007,556-567.
[91] Ehlers M. Spectral Characteristics Preserving Image Fusion Based on Fourier Domain Filtering. Proc.SPIE,2004,5574:1-13.
[92] Klonus S, Ehlers M. Image Fusion Using the Ehlers Spectral Characteristics Preserving Algorithm.GIScience&Rem. Sens.,2007,44:93–116.
[93] Liu J G. Smoothing Filter-based Intensity Modulation: A Spectral Preserve Image Fusion Technique forImproving Spatial Details. International Journal of Remote Sensing,2000,21(18):3461-3472.
[94]李存军，刘良云．两种高保真遥感影像融合方法比较．中国图象图形学报，2004，9(11)：1376-1385．
[95] D D Lee, H S Seung. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature,1999,401(6755):788-791.
[96] D D Lee, H S Seung. Algorithms for Non-negative Matrix Factorization,2000,http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf
[97] Do M N, Vetterli M. The Contourlet Transform: An Efficient Diectional Multiresolution ImageRepresentation. IEEE Trans. Image Processing,2005,14(12):2091-2106.
[98] Miao Qiguang, Wang Baoshu. A Novel Image Fusion Method Using Contourlet Transform.4thInternational Conference on Communications, Circuits and Systems, ICCCAS,2006.
[99] Zhijun Wang, Djemel Ziou, Costas Armenakis, et al. A Comparative Analysis of Image FusionMethods. IEEE Trans. Geoscience and Remote Sensing,2005,43(6):1391-1402.
[100] Pouran Behnia. Comparison between Four Methods for Data Fusion of ETM+Multispectral and PanImages. Geo-spatial Information Science (Quarterly).2005,8(2):98-103.
[101]郭雷，李晖晖．图像融合．北京：电子工业出版社，2008．
[102] J Astola, P Haavisto, Y Neuvo.Vector median filters. Proc. IEEE, Apr.1990,78:678–89.
[103] L Yin, R Yang, M Gabbouj, et al.Weighted median filters:A tutorial. IEEE Trans. Circuits Syst. II,1996,43:157–192.
[104] H G Longbotham, D Eberly. Statistical properties, fixed points, and decomposition with WMMR filters.J. Math. Imag. Vis.,1992,2:99–116.
[105] H G Longbotham, D Eberly. WMMR filters: A class of robust edge enhancers. IEEE Trans. SignalProcessing,1992,41:1680–1684.
[106] L J Christiano, T J Fitzgerald.The Band Pass Filter. International Economic Review,2003,44:435–465.
[107] G Hewer, C Kenney, B S Manjunath. Peer group processing for segmentation.Technical Report#97-01, ECE Dept., UCSB,1997.
[108] Y Deng, C Kenney, M S Moore, et al. Peer group filtering and perceptual color image quantization. inProc. IEEE Int. Symp. Circuits and Systems,1999,4:21–24.
[109] Y Deng, G Hewer, C Kenney, et al. Peer group image processing. Univ. Calif., SantaBarbara, ECE Tech. Rep., Nov.1999.
[110] G Hewer, C Kenney, B Manjunath. Variational image segmentation using boundary functions.IEEE Trans. Image Processing, Sept.1998,7:1269–1282,.
[111] J Morel, S Solimini. Variational Methods in Image Segmentation. Boston, MA: Birkhauser,1995.
[112] D Mumford, J Shah. Boundary detection by minimizing functionals. in IEEE Conf. ComputerVision Pattern Recognition, San Francisco, CA,1985,22–26.
[113] D Mumford, J Shah. Optimal approximation by piecewise smooth functions and associated variationalproblems.Commun. Pure Appl. Math.,1989.
[114] M Oman. Study of variational methods applied to noisy step data.1996.
[115] S Osher, L Rudin. Feature-oriented image enhancement using shock filters.SIAM J. Numer. Anal.,1990,27:919–940.
[116] S Osher, L Rudin. Shocks and other nonlinear filtering applied to image processing.in Proc. SPIE Appl.Dig. Image Proc. XIV,1991,1567:414–430.
[117] L Rudin. Images, numerical analysis of singularities, and shock filters. Ph.D. dissertation, ComputerScience Dept., Caltech, Pasadena, CA,1987.
[118] C Tomasi, R Manduchi. Bilateral Filtering for Gray and Color Images. Proceedings of the1998IEEEInternational Conference on Computer Vision, Bombay, India,1998.
[119] Michael Elad. On the Origin of the Bilateral Filter and Ways to Improve It. IEEE Transactions onImage Processing,2002,11(10):1141-1151,.
[120] P Perona, J Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEETransactions on Pattern Analysis and Machine Intelligence,1990,12(7):629-631.
[121] G Sapiro, D L Ringach. Anisotropic diffusion of color images. Proc. SPIE,1996,471:382–2657.
[122] Black M J, Sapiro G, Marimont D, et al. Robust Anisotropic Diffusion.IEEE Transactions onImage Processing,1998,7(3):421-426.
[123] J Weickert. Anisotropic Diffusion in Image Processing, Tuebner Stuttgart,1998. ISBN3-519-02606-6.
[124] Z Lin, Q Sh. An anisotropic diffusion PDE for noise reduction and thin edge preservation.in Proc.10thInt. Conf. Image Analysis and Processing, IEEE Computer Society, Los Alamitos, CA,1999,102–107.
[125]周光绍，徐勇．在高分辨率遥感影像中提取无清晰连续边缘线的道路．测绘学报，2008，37(3)：301-307．
[126]梅天灿，李德仁，秦前清．基于直线和区域特征的遥感影像线状目标检测．武汉大学学报﹒信息科学版，2005，30(8)：689-693．
[127]程承旗，马廷．高分辨率卫星影像上地物线性特征的自动识别．遥感学报，2003，7(1)：26-30．
[128]明冬萍，骆剑承，周成虎．高分辨率遥感影像特征分割及算法评价分析．地球信息科学，2006，8(1)：103-109．
[129]骆剑承，周成虎，沈占锋，等．遥感信息图谱计算的理论方法研究．地球信息科学学报，2009，11(5)：664-669．
[130] Andreas Koschan, Mongi Abidi.彩色数字图像处理．章毓晋(译)．北京：清华大学出版社，2010．
[131]王志衡，吴福朝．内积能量与边缘检测．计算机学报，2009，32（11）：2211-2221．
[132]林生佑，石教英．基于HSV的彩色图像边缘检测算子．中国图象图形学报，2005，10(1)：43-47．
[133]王小芳，闫光荣，雷毅．彩色图像的复变函数模型及边缘检测．光电工程，2008，35(2)：90-96．
[134]解振东．基于Canny理论的彩色图像边缘检测．物探化探计算技术，2007，29(4)：370-373．
[135]高俊钗，韩冰，王泽民．向量空间彩色图像的Canny边缘检测．科学技术与工程，2008，8(3)：686-689．
[136] Yang C K, Tsai W H. Reduction of color space dimensionality by moment preserving thresholding andits application for edge detection in color images. Pattern Recognition Letters,1996,17(5):481-490.
[137] Wesolkowski S, Jernigan M E, D D Robert. Comparison of color image edge detectors in multiple colorspaces. Proceedings of the International Conference on Image Processing. Vancouver, BC, Canada: IEEE,2000,2:796-799.
[138]王植，贺赛先．一种基于Canny理论的自适应边缘检测方法．中国图象图形学报，2004，9(8)：957-962．
[139] Li Jun ding, Ardeshir Goshtasby. On the Canny edge detector.Pattern Recognition,2001,34:721-725.
[140] Ehlers M. Spectral Characteristics Preserving Image Fusion Based on Fourier Domain Filtering.Proc. SPIE,2004,5574:1-13.
[141] J R Beveridge, J K Griffith, E M Riesman. Segmenting images using localized histograms and regionmerging. International Journal of Computer Vision,1989,2:311–347.
[142] R Adams, L Bischof. Seeded region growing. IEEE Transactions on Pattern Analysis and MachineIntelligence,1994,16(6):641-647.
[143] A Mehnert, P Jackway. An improved seeded region growing algorithm. Pattern Recognition Letters,1997,18(10):1065-1071.
[144] K Haris, S N Efstratiadis, N Maglaveras, et al. Hybrid image segmentation using watersheds and fastregion merging. IEEE Trans. on Image Processing,1998,7(12):1684-1699.
[145] K Fukunaga, L D Hostetler. The Estimation of the Gradient of a Density Function, withApplications in Pattern Recognition. IEEE Trans. on Information Theory,1975,21(1):32-40.
[146] Yizong Cheng. Mean shift, mode seeking, and clustering.IEEE Trans. on Pattern Analysis and MachineIntelligence,1995,17(8):790-799.
[147] D Comaniciu, V Ramesh, P Meer. Real-time tracking of non-rigid objects using mean shift.Proc. IEEEConfe. Computer Vision and Pattern Recognition,2000,2:142-149.
[148] D Comaniciu,V Ramesh, P Meer.The VariableBandwidth Mean Shift and Data-Driven Scale Selection.International Conference on Computer Vision,2001,1:438-445.
[149] D Comaniciu, P Meer. Mean Shift: a robust approach toward feature space analysis. IEEE Trans. onPattern Analysis and Machine Intelligence,2002,24(5):603-619.
[150] D Comaniciu. An algorithm for data-driven bandwidth selection. IEEE Trans. on Pattern Analysis andMachine Intelligence,2003,25(2):281-288.
[151] Falah R, Bolon P, Cocquerez J. A region-region and region-edge cooperative approach of imagesegmentation. In: International Conference on Image Processing, October,Vol.3. Austin, Texas,1994,470–474.
[152] T Pavlidis, Y T Liow. Integrating region growing and edge detection. IEEE Trans. Pattern Anal.Machine Intell.,1990,121:225-233.
[153] J F Haddon, J F Boyce. Image segmentation by unifying region and boundary information. IEEE Trans.Pattern Anal. Machine Intell.,1990,12:929-948.
[154] C C Chu, J K Agganval. The integration of image segmentation maps using region and edgeinformation. IEEE Trans. Pattern Anal. Machine Intell.,1993,15:1241-1252.
[155] J Le Moigne, J C Tilton. Refining image segmentation by integration of edge and region data. IEEETrans. Geoscience and Remote Sensing,1995,33:605-615.
[156] M Tabb, N Ahuja. Multiscale image segmentation by integrated edge and region detection. IEEETrans. Image Process.,1997,6:642-655.
[157] W Y Ma, B S Manjunath. Edge flow: A framework of boundary detection and image segmentation.IEEE Trans.Image Processing,2000,9:1375-1388.
[158] J Fan, D K Y Yau, A K Elmagarmid. Automatic image segmentation by integrating color-edgeextraction and seeded region growing. IEEE Trans. Image Process.,2001,10:1454-1466.
[159] Fukada Y. Spatial clustering procedures for region analysis. Pattern Recognition,1980,12:395-403.
[160] Chen P, Pavlidis T. Image segmentation as an estimation problem. Computer Graphics and ImageProcessing,1980,12:153-172.
[161] Bonnin P, Blanc Talon J. A new edge point/region cooperative segmentation deduced from a3d scenereconstruction application. In: SPIE Applications of Digital Image Processing XII,1989,1153:579-591.
[162] Buvry M, Senard J, Krey C. Hierarchical region detection based on the gradient image. In:Scandinavian Conference on Image Analysis, June, Lappeenranta, Finland,1997,2:717-724.
[163] Buvry M, Zagrouba E, Krey C. A rule-based system for region segmentation improvement instereovision. In:Society of Photo-Optical Instrumentation Engineers, February, Image and Video ProcessingII. San Jose,California,1994,2182:357-367.
[164] Healey G. Segmenting images using normalized color.IEEE Transactions on Systems, Man, andCybernetics,1992,22(1):64-73.
[165] Bertolino P, Montanvert A. Cooperation regions contours multiresolution en segmentation d’image.In:Congres AFCET de Reconnaissance des Formes et Intelligence Artificielle, January, Vol.1. Rennes,France,1996,299-307.
[166] Gevers T, Smeulders A. Combining region growing and edge detection through guided delaunay imagesubdivision.In: IEEE Computer Society Conference on ComputerVision and Pattern Recognition, June,Puerto Rico,1997,1021-1026.
[167] Zucker S. Region growing: Childhood and adolescence.Computer Graphics and Image Processing,1976,5:382-399.
[168] Zucker S. Algorithms for image segmentation. In:Digital Image Processing and Analysis,1977,169-183.
[169] Adams R, Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and MachineIntelligence,1994,16(6):641–647.
[170] Xiaohan Y, Yla Jaaski J. Image segmentation combiningregion growing and edge detection. In:International Conference on Pattern Recognition, August, Vol. C. The Hague,Netherlands,1992,481–484.
[171] Gambotto J. A new approach to combining region growing and edge detection. Pattern RecognitionLetters,1993,14:869–875.
[172] Steudel A, Glesner M. Fuzzy segmented image coding using orthonormal bases and derivative chaincoding.Pattern Recognition,1999,32(11):1827–1841.
[173] Krishnan S, Tan C, Chan K. Closed-boundary extraction of large intestinal lumen. In: InternationalConference of the IEEE Engineering in Medicine and Biology Society, November, Baltimore, Washington,1994.
[174] Lambert P, Carron T. Symbolic fusion of luminancehue-croma features for region segmentation. PatternRecognition,1999,32(11):1857–1872.
[175] Pham D, Prince J. An adaptive fuzzy c-means algorithm for image segmentation in the presence ofintensity inhomogeneities. Pattern Recognition Letters,1999,20(1):57–68.
[176] Moghaddamzadeh A, Bourbakis N. A fuzzy region growing approach for segmentation of color images.Pattern Recognition,1997,30(6):867–881.
[177] Kong S, Kosko B. Image coding with fuzzy image segmentation. In: International Conference on FuzzySystems, March, Vol. I. San Diego, California,1992,213–220.
[178] Bieniek A, Moga A. An efficient watershed algorithm based on connected components. PatternRecognition,2000,33(6):907–916.
[179] De Smet P, Pires R, Vleeschauwer D, et al. Implementation and analysis of an optimized rainfallingwatershed algorithm. Journal of Electronic Imaging,1999,8(3):270–278.
[180] Wang D. A multiscale gradient algorithm for image segmentation using watersheds. PatternRecognition,1997,30(12):2043–2052.
[181] Weickert J. Efficient image segmentation using partial differential equations and morphology. PatternRecognition,2001,34(9):1813–1824.
[182] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. In: International Conference onComputer Vision, June, London, UK,1987,259–268.
[183] Alexander D, Buxton B. Implementational improvements for active region models. In: British MachineVision Conference. Colchester, UK,1997.
[184] Chakraborty A, Staib L, Duncan J. Deformable boundary finding influenced by region homogeneity.In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June, Vol.94. Seattle,Washington,1994,624–627.
[185] Chakrabory A, Duncan J. Game-theoretic integration for image segmentation. IEEE Transactions onPattern Analysis and Machine Intelligence,1999,21(1):12–30.
[186] Mumford D, Shah J. Boundary detection by minimizing functionals. In: IEEE Computer SocietyConference on Computer Vision and Pattern Recognition. San Francisco, California,1985,85:22-26.
[187] Shah J, Pien H, Gauch J. Recovery of surfaces with discontinuities by fusing shading and range datawithin a variational framework. IEEE Transactions on Image Processing,1996,5(8):1243–1251.
[188] Zhu S, Yuille A. Region competition: Unifying snakes, region growing, and bayes/mdl for multi-bandimage segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(9):884–900.
[189] Ronfard R. Region-based strategies for active contours models. International Journal of ComputerVision,1994,13(2):229–251.
[190] Ivins J. Statistical snakes: Active region models. Ph.D.thesis, University of Sheffield,1996.
[191] Ivins J, Porrill J. Active-region models for segmenting textures and colors. Image and VisionComputing,1995,13(5):431–438.
[192] Chesnaud C, Rfrgier P, Boulet V. Statistical region snake-based segmentation adapted to differentphysical noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(11):1145-1157.
[193] Paragios N, Deriche R. Geodesic active regions for supervised texture segmentation. In: InternationalConference on Computer Vision, September, Vol. II. Corfu, Greece,1999,926–932.
[194] Paragios N, Deriche R. Unifying boundary and region-based information for geodesic active tracking.In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June, Vol. II. FortCollins,Colorado,1999,422–427.
[195] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. International Journal of Computer Vision,1997,22(1):61–79.
[196] Will S, Hermes L, Buhmann J, et al. On learning texture edge detectors. In: International Conference onImage Processing, September, Vol. III. Vancouver, Canada,2000,887-880.
[197] Sinclair D. Voronoi seeded colour image segmentation.Tech. Rep.3, AT&T Laboratories Cambridge,1999.
[198] Cufi X, Munoz X, Freixenet J, et al. A concurrent region growing algorithm guided by circumscribedcontours. In: International Conference on Pattern Recognition, September, Vol. I. Barcelona, Spain,2000,432-435.
[199] Gagalowicz A, Monga O. A new approach for image segmentation. In: International Conference onPattern Recognition, November, Paris, France,1986,265-267.
[200] B Wrobel, O Monga. Segmentation d'images naturelles: coopération entre un détecteur contour et undétecteur région. In Actes du Onzième colloque GRETSI, Nice, France, Juin1987.
[201] Philipp S, Zamperoni P. Segmentation and contour closing of textured and non-textured images usingdistances between textures. In: International Conference on Image Processing, September, Vol. C. Lausanne,Switzerland,1996,125-128.
[202] FjOrtoft R, Cabada J, Lopes A, et al. Complementary edge detection and region growing for sar imagesegmentation. In: Conference of the Norwegian Society for Image Processing and Pattern Recognition, MayVol.1. TromsO, Norway,1997,70-72.
[203] Haddon J, Boyce J. Image segmentation by unifying region and boundary information. IEEETransactions on Pattern Analysis and Machine Intelligence,1990,12(10):929-948.
[204] Chu C, Aggarwal J. The integration of image segmentation maps using region and edge information.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(12):1241-1252.
[205] Nair D, Aggarwal J. A focused target segmentation paradigm. In: European Conference on ComputerVision, April, Vol. A. Cambridge, UK,1996,579-588.
[206] Sato M, Lakare S, Wan M, et al. A gradient magnitude based region growing algorithm for accuratesegmentation. In: International Conference on Image Processing, September, Vol. III. Vancouver, Canada,2000,448-451.
[207] Bergholm F. Edge focusing. IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(6):726-741.
[208] Spann M, Wilson R. A quad-tree approach to image segmentation which combines statistical andspatial information.Pattern Recognition,1985,18(3/4):257-269.
[209] Spann M, Wilson R. A new approach to clustering.Pattern Recognition,1990,23(12):1413-1425.
[210] Wilson R, Spann M. Finite prolate spheroidial sequences and their applications. II. Image featuredescription and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(2):193-203.
[211] Hsu T, Kuo J, Wilson R. A multiresolution texture gradient method for unsupervised segmentation.Pattern Recognition,2000,32(11):1819-1833.
[212] Chan F, Lam F, Poon P, et al. Object boundary location by region and contour deformation.IEEEProceedings––Vision Image and Signal Processing,1996,143(6):353-360.
[213] Verard L, Fadili J, Ruan S, et al.3d mrisegmentation of brain structures. In: International Conference ofthe IEEE Engineering in Medicine and Biology Society, November, Amsterdam, Netherlands,1996,1081-1082.
[214] Jang D, Lee D, Kim S. Contour detection of hippocampus using dynamic contour model and regiongrowing. In: International Conference of the IEEE Engineering in Medicine and Biology Society, October,Chicago,Ilinois,1997,763–766.
[215] Fua P, Hanson A. Using generic geometric models for intelligent shape extraction. In: NationalConference on Artificial Intelligence, July, Seattle, Washington,1987,706-711.
[216] Lemoigne J, Tilton J. Refining image segmentation by integration of edge and region data. IEEETransactions on Geoscience and Remote Sensing,1995,33(3),605-615.
[217] Hojjatoleslami S, Kittler J. Region growing: A new approach. IEEE Transactions on Image Processing,1998,7(7):1079-1084.
[218] Siebert A. Dynamic region growing. In: Vision Interface,May, Kelowna, Canada,1997.
[219] Revol Muller C, Peyrin F, Odet C, et al. Automated3d region growing algorithm governed by anevaluation function. In: International Conference on Image Processing, September, Vol. III. Vancouver,Canada,2000,440-443.
[220]胡晓东，骆剑承，沈占锋，等．高分辨率遥感影像并行分割结果缝合算法．遥感学报，2010，14(5)：917-927．