面向对象的遥感影像变化检测技术研究
|
摘要
遥感影像的变化检测技术在国民经济和国防建设中具有广泛的应用。利用同一地区的多时相遥感影像进行变化检测可以提取、定位该区域在不同时相间的地表变化信息,并且能够定量的对变化信息及变化过程进行比较分析。利用变化检测技术获取的结果可以用于地理数据更新及应用、自然灾害监测评估及预报、战场态势分析以及打击效果评估等多个邻域。
本文主要围绕从不同时相遥感影像快速有效提取变化信息展开研究,对面向对象的遥感影像变化检测所涉及的相关理论和方法进行了深入探讨。本文的主要工作和创新点如下:
1.对变化检测的现状进行了研究,总结了变化检测方法所存在的问题。深入分析了对象的表达、特性及应用,并归纳和总结了对象分割的方法。将面向对象影像分析方法应用于多时相遥感影像变化检测。
2.提出了基于地物空间结构的误匹配剔除方法。针对遥感影像匹配出现的误匹配情况,在顾及变化检测的两幅遥感影像未变化区域具有相同或相似的相对稳定空间结构信息基础上,提出了在待配准影像中通过构建三角形的方式模拟地物空间结构的方法来剔除误匹配点。并通过实验证明了该方法的有效性。
3.以对象的提取为目标,重点讨论了将影像划分成内部特性相对均一、相互之间有所差异的影像对象的不同分割方法。研究了均值漂移法和分形网络演化法。将分水岭方法引入到面向对象的变化检测分割中,针对分水岭方法过度分割问题,提出在梯度图像上进行分割处理,再进行滤波然后采用基于局部同质性合并小区域,实验表明过度分割大大减少,较好的实现了对象的提取。
4.将变化向量分析法应用于面向对象的变化检测。在对象变化向量分析法中,讨论了利用层间逻辑值对变化强度进行描述的方法。为了快速、有效的获取影像变化信息,采用二维最大类间方差进行变化阈值的自动获取,考虑到二维最大类间方法的速度较慢,提出了一种自动提取阈值的快速计算方法,并通过实验验证了该方法在对象级变化检测中的有效性。
论文中对所提出的各种算法利用不同多时相遥感影像进行实验,开发了一个面向对象的变化检测演示验证系统。实验表明采用面向对象的变化检测方法整体上比基于像素的变化检测方法效果要好。
Change detection of remote sensing images has been applied extensively in civil and military areas. By analyzing the multi-temporal remote sensing images, change detection can be used to extract and locate the ground change information and it can also be used to analyze the change information and change progress quantitatively. The extracted change information can be used in updating geography date and application, monitoring evaluating and predicting natural disasters, as well as battlefield situation analysis and evaluation of strike effectiveness.
This dissertation mainly studies that how do automatic extract changed information from multi-temporal remote sensing images, which discuss deeply some theory and methods on Object-Oriented remote sensing images change detection. The major innovations of this dissertation are listed as follows:
1. The development background and status of change detection are discussed, existing problems of change detection approaches and theory are summarized. The expression, attribute and application of objects are deeply analyzed. The segmentation of object are concluded and summarized in the round. Object-oriented image analysis method was applied to multi-temporal remote sensing image change detection.
2. Spatial structure is proposed based on the Error matching method. Aimed to the Error matching, taking into account the change detection does not change in two areas of remote sensing images, with the same or similar spatial structure of relative stability based on the information presented in the pending registration. Via constructing a triangle image to simulate the spatial structure of surface features to eliminate false matching points. Experiments show the effectiveness for the method.
3. In order to extract object, focusing on dividing the image into internal characteristics relatively homogeneous, the difference between them. A lot image objects are segmented. In this paper, firstly discuss the mean shift method and the fractal net evolution approach. Then watershed method is imported to object-oriented change detection segmentation, Aimed to watershed approach have the immoderacy segmentation problem, Then propose in the gradient image processing, while filter and unite small areas based on local homogeneity. Experiment proved the notable reduction of excessive division and the extraction of object is achieved.
4. The change vector analysis is applied to Object-Oriented change detection of remote sensing images. The method of expression variation intension by making use of logicals of layer is discussed of object change vector analysis. In order to obtain the changing intensity of the image rapidly and effectively, the automatic obtain of changes threshold by making use of two-dimensional Otsu. The rapid calculation method is proposed according to the slow speed of two-dimensional Otsu. Experiment proved the feasibility of this method.
All the arithmetic are experimented by making use of multi-temporal remote sensing images, and exploit an object-oriented change detection system. Object-oriented approach is better than the pixel-based change detection method.
本文主要围绕从不同时相遥感影像快速有效提取变化信息展开研究,对面向对象的遥感影像变化检测所涉及的相关理论和方法进行了深入探讨。本文的主要工作和创新点如下:
1.对变化检测的现状进行了研究,总结了变化检测方法所存在的问题。深入分析了对象的表达、特性及应用,并归纳和总结了对象分割的方法。将面向对象影像分析方法应用于多时相遥感影像变化检测。
2.提出了基于地物空间结构的误匹配剔除方法。针对遥感影像匹配出现的误匹配情况,在顾及变化检测的两幅遥感影像未变化区域具有相同或相似的相对稳定空间结构信息基础上,提出了在待配准影像中通过构建三角形的方式模拟地物空间结构的方法来剔除误匹配点。并通过实验证明了该方法的有效性。
3.以对象的提取为目标,重点讨论了将影像划分成内部特性相对均一、相互之间有所差异的影像对象的不同分割方法。研究了均值漂移法和分形网络演化法。将分水岭方法引入到面向对象的变化检测分割中,针对分水岭方法过度分割问题,提出在梯度图像上进行分割处理,再进行滤波然后采用基于局部同质性合并小区域,实验表明过度分割大大减少,较好的实现了对象的提取。
4.将变化向量分析法应用于面向对象的变化检测。在对象变化向量分析法中,讨论了利用层间逻辑值对变化强度进行描述的方法。为了快速、有效的获取影像变化信息,采用二维最大类间方差进行变化阈值的自动获取,考虑到二维最大类间方法的速度较慢,提出了一种自动提取阈值的快速计算方法,并通过实验验证了该方法在对象级变化检测中的有效性。
论文中对所提出的各种算法利用不同多时相遥感影像进行实验,开发了一个面向对象的变化检测演示验证系统。实验表明采用面向对象的变化检测方法整体上比基于像素的变化检测方法效果要好。
Change detection of remote sensing images has been applied extensively in civil and military areas. By analyzing the multi-temporal remote sensing images, change detection can be used to extract and locate the ground change information and it can also be used to analyze the change information and change progress quantitatively. The extracted change information can be used in updating geography date and application, monitoring evaluating and predicting natural disasters, as well as battlefield situation analysis and evaluation of strike effectiveness.
This dissertation mainly studies that how do automatic extract changed information from multi-temporal remote sensing images, which discuss deeply some theory and methods on Object-Oriented remote sensing images change detection. The major innovations of this dissertation are listed as follows:
1. The development background and status of change detection are discussed, existing problems of change detection approaches and theory are summarized. The expression, attribute and application of objects are deeply analyzed. The segmentation of object are concluded and summarized in the round. Object-oriented image analysis method was applied to multi-temporal remote sensing image change detection.
2. Spatial structure is proposed based on the Error matching method. Aimed to the Error matching, taking into account the change detection does not change in two areas of remote sensing images, with the same or similar spatial structure of relative stability based on the information presented in the pending registration. Via constructing a triangle image to simulate the spatial structure of surface features to eliminate false matching points. Experiments show the effectiveness for the method.
3. In order to extract object, focusing on dividing the image into internal characteristics relatively homogeneous, the difference between them. A lot image objects are segmented. In this paper, firstly discuss the mean shift method and the fractal net evolution approach. Then watershed method is imported to object-oriented change detection segmentation, Aimed to watershed approach have the immoderacy segmentation problem, Then propose in the gradient image processing, while filter and unite small areas based on local homogeneity. Experiment proved the notable reduction of excessive division and the extraction of object is achieved.
4. The change vector analysis is applied to Object-Oriented change detection of remote sensing images. The method of expression variation intension by making use of logicals of layer is discussed of object change vector analysis. In order to obtain the changing intensity of the image rapidly and effectively, the automatic obtain of changes threshold by making use of two-dimensional Otsu. The rapid calculation method is proposed according to the slow speed of two-dimensional Otsu. Experiment proved the feasibility of this method.
All the arithmetic are experimented by making use of multi-temporal remote sensing images, and exploit an object-oriented change detection system. Object-oriented approach is better than the pixel-based change detection method.
引文
[1] Bovolo and Bruzzone. A theoretical framework for unsupervised change detection on change vector analysis in the polar domain[J], Transactions on Geoscience and Remote Sensing,2007,45(1):218-235.
[2] Richard. Image change detection algorithms[J],2005,14(3):294-307.
[3] Lu,D. Change detection techniques[J],International Journal of Remote Sensing,2004,25(12):2365-2407.
[4] SinghA. Digital change detection techniques using remotely-sensed date[J], International Journal of Remote Sensing,2004,10(6):989-1003.
[5]李德仁.利用遥感进行变化检测[J].武汉大学学报信息科学版,2003,28(5):7-12.
[6]朱朝杰.基于特征分析的遥感图像变化检测方法研究[D].解放军信息工程大学测绘学院硕士论文,2007
[7]莫华.遥感影像上军事目标变化检测相关关键技术研究[D].解放军信息工程大学测绘学院硕士论文,2007
[8]陈科.基于判别分析的遥感图像变化检测方法研究[D].解放军信息工程大学测绘学院硕士论文2009
[9]马国锐.一种遥感影像核变化检测方法[J].武汉大学学报信息科学版,2007,32(7);579-600
[10]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[11]陈秋晓.高分辨率遥感影像分类技术研究[D].中国科学院研究生院,2004.
[12] Coppin P Bauer M. Digital change detection in forest ecosystems with remote sensing imagery, Remote Sens.Rev.vol.13,pp,207-304,1996.
[13]眭海刚.基于特征的道路网变化检测方法研究[D].武汉:武汉大学博士学位论文,2002.
[14]陈志鹏.基于纹理特征的差值变化检测方法研究[D].中国科学院研究生院硕士学位论文,2002.6.
[15]张晓东.基于遥感影像与GIS数据的变化检测理论和方法研究[D].武汉大学博士学位论文,2005.4.
[16]钟家强.基于多时相遥感图像的变化检测[D].国防科学技术大学博士学位论文,2005.
[17]马国瑞.核方法之于遥感影像变化检测研究[D].武汉:武汉大学博士学位论文,2007.
[18] ZHAO Y. Monitoring technology of salinity in water with optical fibre sensor[J],Journal of Lightwave Techology,2003,21(5):1334-1338.
[19] Deer P. Digital Change Detection Technique:Civilian and Military Application.
[20] Ridd M K A comparison of four algorithms for change detection in an urban environment[J],Remote Sensing of Enviroment,1998,62:95-100.
[21] ANGELICI,G.. Techniques for land use change detection using Landsat imagery[J], Proceeding of the 43rd Annual Meeting of the American Society od Photogrammetry and Joint Symposium om land date Systems,1977,USA:217-228.
[22] PILON P. An enhanced classification approach to change detection in semi-arid environments[J],Photogrametric Engineering and Remote Sensing,1988,54(12):1709-1716.
[23] CHAVEZ,P.S. Auotomatic detection of vegetation changes in the southwestern Unites States using remotely sensed images[J], Photogrammetric Engineering and Remote Sensing,1994, 60:571-583.
[24] SUNAR,F. An analysis of change in a multi-date set[J], International Journal of Romete-Sensing,1998,19(2):225-235.
[25] LIU X. Urban change detection based on an artifical neural network[J],International Joural of Remote Sensing,23(12):2513-2518.
[26] LAMBIN E F. Time Series of remote sensing date for land change science[J],IEEE Transations on Geoscience and Remote Sensing,44(7):1926-1928.
[27]张路.基于多元统计分析的遥感影像变化检测方法研究[D],武汉大学,2004.
[28]王树根.摄影测量原理与应用[M],武汉大学出版社,2009.
[29] Kettig R.L, Landgrede D.A. Classification of multispectral image date by extraction and classification of homogeneous objects. IEEE Transaction on Geoscience Electronics, 14(1):19-26,1976.
[30]周成虎骆剑承.高分辨率卫星遥感影像地学计算[M].科学出版社,2009.
[31] Blaschke T, Lang S, Lorup E. Objiect-oriented image processing in an integrated GIS/remote sensing environment and perspective for environmental allocations[J]. Environmental Information for Planning.2000.
[32] Hellwich O, Wiedemann C. Object extraction from High-Resolution multisensor image date[J]. In 3rd International conference on fusion of earth, France,26-28,SEE GreCA,Nice:105-115.
[33] Hazel. Object-lever change detection in spectral imagery[J].TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL.39,NO.3,MARCH.2001.
[34] Walter. Object-based classification of remote sensing date for change detection. ISPRS Journal of Photogrammetry & Remote Sensing 225-238,2004.
[35] Li Jia-cun ,Qian Shao-meng Chen Xue. Obiect-Oriented Method of Land Cover Change Detection Approach Using High Spatial Resolution Remote Sensing Date[J].IEEE,2003.
[36] Desclee, Bogaert. Forest change detection by statistical object-based method. Remote Sensing of Environment, 1-11,2006.
[37]贾永红,计算机图像处理与分析[M],武汉:武汉大学出版社,2001.
[38]边肇祺,张学工.模式识别(第二版)[M],北京:清华大学出版社,2000
[39]李云.特征选择算法及其在基于内容图像检索中的应用研究[D].重庆大学博士论文,2005
[40]李弼程,彭天强,彭波等.智能图像处理技术[M],北京:电子工业出版社,2004.
[41]黄晶.基于分形维度与灰度共生矩阵的图像分类研究[D],武汉理工大学,2008.
[42]王建梅.面向对象的高分辨率遥感影像分类与变化检测[D].武汉大学, 2008.
[43]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2006.
[44]陈启浩.面向对象的多源遥感数据分类技术研究与实现[D].中国地质大学,2006.
[45] Chen S H, Yeh C H. On the emergent properties of artificial stock market [J]. Journal of Economic Behavior and Organization,49(2):217-239,2002.
[46] LeBaron B. Evolution and time horizons in an agert-based stock market [J]. Macroeconomic Dynamics,5(2):225-254,2001.
[47]王鹏伟.基于多尺度理论的图像分割方法研究[D].中国科学技术大邪恶,2007.
[48] D. AG, "Definiens Developer 7 User Guide,"[EB]. München: Definiens AG, 2007.
[49]耿则勋,张保明,范大昭.数字摄影测量学[M].测绘出版社,2010.
[50]唐永鹤.基于特征点的匹配算法研究[D].国防科学技术大学硕士论文,2007.
[51] David Lowe.Local feature view clustering for 3D object recognition[EB/OL].
[52] David G Lowe. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[53]钦伟瑾.基于对象的城市遥感影像分类方法及应用研究[D].长安大学硕士论文, 2009.
[54] Comaniciu D,Meer P.Mean Shift: A Robust Approach toward Feature Space Analysis [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002.24(5) :603-619.
[55] Fukunaga K,Hostetler.L.D.The estimation of the gradient of a density function with applications in pattern recognition[J].IEEE Trans Informatioon Theory,1975,21:32-40.
[56] Cheng Y.Mean Shift Mode Seeking and Clustering[J].IEEE Trans Pattern Anal Mach Intell,1995,17(8):790-799.
[57] Baatz M, Sch?pe A. Multiresolution Segmentation:an optimization approach for high quality multi-scale image segmentation.Angewandte Geographische Informations verarbeitung[J],14(6):12-17,2001.
[58]刘杰.基于分水岭与区域生长的彩色图像分割算法研究[D].湖南师范大学,2009.
[59]陈婷婷,程小平.采用模糊形态学和形态学分水岭算法的图像分割[J].西南大学学报(自然科学版), 2008.
[60]王凤娥.改进后的分水岭算法在图像分割中的应用研究[D].山东大学, 2008.
[61]种伟亮.基于分水岭算法的医学影像分析[D].上海交通大学, 2007.
[62]张俊宝,陈红林.最大类间方差法在图像处理中的应用[J].无线电工程, 2006,(7):25-26.
[63]景晓军,蔡安妮,孙景鳌.基于微粒子群理论的二维最大类间方差阈值分割算法[J].弹箭与制导学报, 2009,(3):247-250.
[64]吴一全,吴文怡,潘喆.二维最大类间方差阈值分割的快速迭代算法[J].中国体视学与图像分析, 2007,(3):216-220.
[65]赵凤,范九伦.一种结合二维Otsu法和模糊熵的图像分割方法[J].计算机应用研究, 2007,24(6):189-191.
[66] Hay, Marceau. A Multiseaie Framework for Ldscape Analysis, objeet-speeifieanaly sisandu Pseaiing[J].2001.
[67]黄慧萍,面向对象影像分析中的尺度问题研究[D].中国科学院研究生院遥感应用所,2003.
[2] Richard. Image change detection algorithms[J],2005,14(3):294-307.
[3] Lu,D. Change detection techniques[J],International Journal of Remote Sensing,2004,25(12):2365-2407.
[4] SinghA. Digital change detection techniques using remotely-sensed date[J], International Journal of Remote Sensing,2004,10(6):989-1003.
[5]李德仁.利用遥感进行变化检测[J].武汉大学学报信息科学版,2003,28(5):7-12.
[6]朱朝杰.基于特征分析的遥感图像变化检测方法研究[D].解放军信息工程大学测绘学院硕士论文,2007
[7]莫华.遥感影像上军事目标变化检测相关关键技术研究[D].解放军信息工程大学测绘学院硕士论文,2007
[8]陈科.基于判别分析的遥感图像变化检测方法研究[D].解放军信息工程大学测绘学院硕士论文2009
[9]马国锐.一种遥感影像核变化检测方法[J].武汉大学学报信息科学版,2007,32(7);579-600
[10]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[11]陈秋晓.高分辨率遥感影像分类技术研究[D].中国科学院研究生院,2004.
[12] Coppin P Bauer M. Digital change detection in forest ecosystems with remote sensing imagery, Remote Sens.Rev.vol.13,pp,207-304,1996.
[13]眭海刚.基于特征的道路网变化检测方法研究[D].武汉:武汉大学博士学位论文,2002.
[14]陈志鹏.基于纹理特征的差值变化检测方法研究[D].中国科学院研究生院硕士学位论文,2002.6.
[15]张晓东.基于遥感影像与GIS数据的变化检测理论和方法研究[D].武汉大学博士学位论文,2005.4.
[16]钟家强.基于多时相遥感图像的变化检测[D].国防科学技术大学博士学位论文,2005.
[17]马国瑞.核方法之于遥感影像变化检测研究[D].武汉:武汉大学博士学位论文,2007.
[18] ZHAO Y. Monitoring technology of salinity in water with optical fibre sensor[J],Journal of Lightwave Techology,2003,21(5):1334-1338.
[19] Deer P. Digital Change Detection Technique:Civilian and Military Application.
[20] Ridd M K A comparison of four algorithms for change detection in an urban environment[J],Remote Sensing of Enviroment,1998,62:95-100.
[21] ANGELICI,G.. Techniques for land use change detection using Landsat imagery[J], Proceeding of the 43rd Annual Meeting of the American Society od Photogrammetry and Joint Symposium om land date Systems,1977,USA:217-228.
[22] PILON P. An enhanced classification approach to change detection in semi-arid environments[J],Photogrametric Engineering and Remote Sensing,1988,54(12):1709-1716.
[23] CHAVEZ,P.S. Auotomatic detection of vegetation changes in the southwestern Unites States using remotely sensed images[J], Photogrammetric Engineering and Remote Sensing,1994, 60:571-583.
[24] SUNAR,F. An analysis of change in a multi-date set[J], International Journal of Romete-Sensing,1998,19(2):225-235.
[25] LIU X. Urban change detection based on an artifical neural network[J],International Joural of Remote Sensing,23(12):2513-2518.
[26] LAMBIN E F. Time Series of remote sensing date for land change science[J],IEEE Transations on Geoscience and Remote Sensing,44(7):1926-1928.
[27]张路.基于多元统计分析的遥感影像变化检测方法研究[D],武汉大学,2004.
[28]王树根.摄影测量原理与应用[M],武汉大学出版社,2009.
[29] Kettig R.L, Landgrede D.A. Classification of multispectral image date by extraction and classification of homogeneous objects. IEEE Transaction on Geoscience Electronics, 14(1):19-26,1976.
[30]周成虎骆剑承.高分辨率卫星遥感影像地学计算[M].科学出版社,2009.
[31] Blaschke T, Lang S, Lorup E. Objiect-oriented image processing in an integrated GIS/remote sensing environment and perspective for environmental allocations[J]. Environmental Information for Planning.2000.
[32] Hellwich O, Wiedemann C. Object extraction from High-Resolution multisensor image date[J]. In 3rd International conference on fusion of earth, France,26-28,SEE GreCA,Nice:105-115.
[33] Hazel. Object-lever change detection in spectral imagery[J].TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL.39,NO.3,MARCH.2001.
[34] Walter. Object-based classification of remote sensing date for change detection. ISPRS Journal of Photogrammetry & Remote Sensing 225-238,2004.
[35] Li Jia-cun ,Qian Shao-meng Chen Xue. Obiect-Oriented Method of Land Cover Change Detection Approach Using High Spatial Resolution Remote Sensing Date[J].IEEE,2003.
[36] Desclee, Bogaert. Forest change detection by statistical object-based method. Remote Sensing of Environment, 1-11,2006.
[37]贾永红,计算机图像处理与分析[M],武汉:武汉大学出版社,2001.
[38]边肇祺,张学工.模式识别(第二版)[M],北京:清华大学出版社,2000
[39]李云.特征选择算法及其在基于内容图像检索中的应用研究[D].重庆大学博士论文,2005
[40]李弼程,彭天强,彭波等.智能图像处理技术[M],北京:电子工业出版社,2004.
[41]黄晶.基于分形维度与灰度共生矩阵的图像分类研究[D],武汉理工大学,2008.
[42]王建梅.面向对象的高分辨率遥感影像分类与变化检测[D].武汉大学, 2008.
[43]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2006.
[44]陈启浩.面向对象的多源遥感数据分类技术研究与实现[D].中国地质大学,2006.
[45] Chen S H, Yeh C H. On the emergent properties of artificial stock market [J]. Journal of Economic Behavior and Organization,49(2):217-239,2002.
[46] LeBaron B. Evolution and time horizons in an agert-based stock market [J]. Macroeconomic Dynamics,5(2):225-254,2001.
[47]王鹏伟.基于多尺度理论的图像分割方法研究[D].中国科学技术大邪恶,2007.
[48] D. AG, "Definiens Developer 7 User Guide,"[EB]. München: Definiens AG, 2007.
[49]耿则勋,张保明,范大昭.数字摄影测量学[M].测绘出版社,2010.
[50]唐永鹤.基于特征点的匹配算法研究[D].国防科学技术大学硕士论文,2007.
[51] David Lowe.Local feature view clustering for 3D object recognition[EB/OL].
[52] David G Lowe. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[53]钦伟瑾.基于对象的城市遥感影像分类方法及应用研究[D].长安大学硕士论文, 2009.
[54] Comaniciu D,Meer P.Mean Shift: A Robust Approach toward Feature Space Analysis [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002.24(5) :603-619.
[55] Fukunaga K,Hostetler.L.D.The estimation of the gradient of a density function with applications in pattern recognition[J].IEEE Trans Informatioon Theory,1975,21:32-40.
[56] Cheng Y.Mean Shift Mode Seeking and Clustering[J].IEEE Trans Pattern Anal Mach Intell,1995,17(8):790-799.
[57] Baatz M, Sch?pe A. Multiresolution Segmentation:an optimization approach for high quality multi-scale image segmentation.Angewandte Geographische Informations verarbeitung[J],14(6):12-17,2001.
[58]刘杰.基于分水岭与区域生长的彩色图像分割算法研究[D].湖南师范大学,2009.
[59]陈婷婷,程小平.采用模糊形态学和形态学分水岭算法的图像分割[J].西南大学学报(自然科学版), 2008.
[60]王凤娥.改进后的分水岭算法在图像分割中的应用研究[D].山东大学, 2008.
[61]种伟亮.基于分水岭算法的医学影像分析[D].上海交通大学, 2007.
[62]张俊宝,陈红林.最大类间方差法在图像处理中的应用[J].无线电工程, 2006,(7):25-26.
[63]景晓军,蔡安妮,孙景鳌.基于微粒子群理论的二维最大类间方差阈值分割算法[J].弹箭与制导学报, 2009,(3):247-250.
[64]吴一全,吴文怡,潘喆.二维最大类间方差阈值分割的快速迭代算法[J].中国体视学与图像分析, 2007,(3):216-220.
[65]赵凤,范九伦.一种结合二维Otsu法和模糊熵的图像分割方法[J].计算机应用研究, 2007,24(6):189-191.
[66] Hay, Marceau. A Multiseaie Framework for Ldscape Analysis, objeet-speeifieanaly sisandu Pseaiing[J].2001.
[67]黄慧萍,面向对象影像分析中的尺度问题研究[D].中国科学院研究生院遥感应用所,2003.