高分影像空间结构特征建模与信息提取
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摘要
近十年来高分辨率对地观测技术得到了迅猛发展,已成为当前世界高科技发展的前沿领域。高分辨率商业卫星的空间分辨率已达到米级或亚米级的水平,能够获取更详细的地面信息,如何实现高分影像解译与地物信息提取已成为当前重要的研究课题。空间结构特征是高分影像最显著的特征之一,高分影像上的各种地物类型都表现出不同程度的结构特性,这些结构既包括纹理的、几何的也包括空间关系的。有效利用高分影像的空间结构信息对于影像的解译与地物信息的提取将具有重要的作用。本文以高分影像的空间结构特征建模与信息提取为主题,系统研究了基于空间统计和数据场的高分影像空间结构特征描述、建模与提取方法,并将获取的空间结构特征与结构模型用于改善高分影像的分类或实现居民区的检测。
本文的主要研究工作如下：
(1)对高分影像空间结构特征的概念、内涵和特点进行了探讨。借鉴系统科学对结构的认识,本文从影像的组成“元素”以及这些元素之间的“空间关系”来认识影像的空间结构。这里的“元素”是结构分析的基本单元,它既与影像自身的空间分辨率有关,也与人们对事物的认知模式密切相关。考虑到空间结构的多尺度特性,本文提出了高分影像空间结构特征的一个多层次分析框架,该框架将高分影像的空间结构划分为像元结构、目标结构和场景结构三个不同的层次。然后在该框架下,重点了研究高分影像在前两个层次的空间结构特征,并分别研究了基于空间自相关统计、空间半变差函数和数据场的空间结构特征建模方法。
(2)研究了基于空间自相关统计的高分影像空间结构描述方法。首先分析了空间自相关统计量对高分影像中不同地物空间依赖的响应。实验发现：Moran'sⅠ和Getis统计量对影像中的同质性区域响应较好,并且后者还能区分是高亮同质区还是低暗同质区；Geary's C统计量对影像中的异质性区域或边缘区域响应较好。进一步,根据影像中不同区域空间关联强度的差异,使用局部空间统计量和3D阈值分割法实现了高分影像中同质区和边缘结构的提取。接下来,本文将六个空间自相关统计量(包括三个全局统计量和三个局部空间统计量)用于高分影像局部空间结构的描述,并将提取的空间结构特征与光谱特征融合用于高分影像的分类,通过比较分类结果对各统计量的局部空间结构的描述能力进行了比较分析。实验发现：Moran's I和Getis统计量在改善影像中同质性地物的分类效果较好,同时Getis统计量还具有一定的平滑滤波的作用；Geary's C统计量能够显著改善影像中的异质性较大的地物类(如建筑区和植被)的分类结果；在相同的邻域窗口条件下,全局统计量能够比局部统计量获取更多的空间结构信息,但是其计算量会显著增大。最后,针对传统的空间自相关统计量描述高分影像局部空间结构信息的不足,本文提出了基于方向空间关联性的特征描述方法。该方法利用了方向射线,能够同时考虑空间关联的强度信息和长度信息,与通常的基于窗口的描述方法相比,它不仅能够提取高分影像局部的空间变异或表面纹理信息,还能够利用地物类的形状和结构信息,同时它还能够减少计算代价。与传统的基于窗口的Moran's I、Geary's C、灰度共生矩阵和小波变换方法进行比较,本文提出的方法都表现出了较优越的性能。
(3)研究了基于空间半变差函数的高分影像空间结构特征建模与居民区提取方法。本文将半变差函数的形态分为“平衡型”、“不平衡型”和“周期型”三种类型,并且给出了描述半变差函数曲线形态的多个参数,这些参数可用于描述高分影像的空间结构特征。进一步,本文提出了一种基于半变差的高分影像居民区检测方法。该方法利用半变差函数来建模居民区的空间结构特征,实验发现,高分影像中居民区的半变差函数符合具有一定周期的孔穴效应模型。基于该模型本文提取了表征居民区结构的特征参数,并利用该特征通过对高分影像进行基于分块的分类实现居民区的检测。
(4)研究了基于数据场的高分影像空间结构建模与居民区提取方法。首先比较了数据场方法与空间统计学中局部空间统计量之间的关系,本文获得了以下结论：通过选择合适的空间权重,局部空间统计量可以统一到数据场势函数体系,或者说局部空间统计量可以作为数据场描述数据对象相互作用的一种势函数。然后借鉴局部空间统计量描述空间关联性的方法,将数据场方法用于高分影像局部空间结构特征的提取,提取的新特征改善了高分影像的分类。最后,将数据场方法用于居民区空间结构的描述与建模,提出了一种基于数据场的高分影像居民区检测方法。该方法将居民区中的建筑物或其部分特征(如特征点、特征线)看成是质点,居民区内部的空间结构通过场来进行描述和建模；进一步,根据居民区与非居民区在势值上的差异,通过阈值分割的方式实现居民区的分割,并通过“噪声”去除、孔洞填充等方法完善了居民区的提取结果。
High-resolution earth observation technology has obtained rapid development over the past decade, and at present, it has become one of the frontiers of high-tech development in the world. With a spatial resolution of meter or sub-meter level, high-resolution images can provide more detailed ground information and how to achieve the image interpretation and information extraction has become an important research topic. Spatial structural feature is one of the most significant spatia features of high resolution images, in which most of the object types exhibit different degrees of structural characteristics such as textural structure, geometrc structure and spatial relationships. It is well known that effectively utilizing spatial structural information can play an important role in impoving the image classfication result and increasing the object detection accuracy. In this paper, we studied the methods of spatial structural feature description, modeling and extraction based on spatial statistics and data field. The structural features or structural model obtained by the proposed methods were then applied to improving the image classification or achieving the detection of residential area class.
The main work of this paper are as follows:
(1) Discussed the concept, connotation and characteristics of spatial structural features in high resolution images. Drawing on the theory of system science which includes the cognition and understanding of structure, we recognized the spatial structure of an image from two aspects:one is the composed "element" of the image, and the other is the "spatial structural relationship" among these elements. Here the "element" is the basic unit of structure analysis, which is closely related to the spatial resolution of the image itself as well as human cognitive pattern of things. Taking into account the multi-scale characteristics of spatial structure, this paper proposed a multi-level analysis framework of spatial structure, which divided the spatial structure of high resolution images into three different levels including pixel structure, object structure and scene structure. In this framework, this paper focused on the first two levels of spatial structure, and studied the methods of spatial structural feature modeling based on spatial autocorrelation statistics, semi-variance function and data field, respectively.
(2)Studied the methods of spatial structural feature description based on spatial autocorrelation statistics. First, we tested the response characteristics of spatial autocorrelation statistics to spatial dependance of different object classes in high resolution images. It was found that, Moran's I and Getis statistics can make a good response to homogeneous regions in an image, and the latter can distinguish between homogeneous regions of high value and low value; Geary's C statistic can make a strong response to heterogeneous regions or edges in the image. Further, according to intensity differences of spatial correlation in different regions in the image, this paper proposed a novel method of extracting homogeneous regions and edge structure from images using local spatial statistics. Second, the well-known six spatial autocorrelation statistics (including three global statistics and three local spatial statistics) were applied to the local spatial structural feature description and extraction of high resolution image, and the extracted spatial features were integrated with the spectral features for image classification. By comparing the classification results of using each statistic, the performance of each statistic was evaluated. Experimental results indicate that Moran's I and Getis statistics can better improve the classification of homogeneous object classes, while Geary's C statistic can significantly improve the classification of heterogeneous object classes (such as built-up areas and vegetation). It was also found that within the same neighborhood window, global statistics can get more spatial structural information but also need more computation cost than local statistics. Finally, to overcome some shortcomings of spatial autocorrelation statistics in the local spatial structure description, this paper proposed a novel method of modeling spatial structural features using directional spatial correlation(DSC). This method used eight directional half-lines instead of a window to measure spatial correlation in neighborhoods of pixels, which can take into account both the intensity and the length of spatial correlation. Compared with the usual window-ased methods, the proposed method can not only extract the information of spatial variability or surface texture but also can use the geometric information about shape and structure of object classes. In particular, it can also reduce the computation cost. By comparison, it was found the proposed method outperform some existing algorithms including Moran's I, Geary's C, GLCM, and showed competitive performance against wavelet transform based methods in accuracy or computation time.
(3) Studied the methods of spatial structural feature modeling and residential area extraction based on based on semi-variance function. This paper first divided the common semi-variograms into three different types:"balanced","unbalanced" and "periodic", and then studied the spatial structural feature extraction method based on parameters characterizing semi-variogram curve shape. Then, a novel method of residential areas detection based on semi-variance function was proposed. This method used semi-variance function to modeling the spatial structure of residential areas, and it was found by experiments that the semi-variogram of residential areas is subject to the hole effect model with a certain period. Based on this model, this paper extracted feature parameters characterizing the spatial structure of residential areas, and further the extracted features were applied to the detection of residential areas.
(4) Studied the methods of spatial structural feature modeling and residential area extraction based data field. First, this paper studied the relationship between the local spatial statistics in spatial statistics and the data field, and found that by selecting appropriate spatial weights, local spatial statistics can be unified into the potential function system of data field. In other words, local spatial statistics can be used as a potential function of data field, describing the interaction between data objects. Second, drawing on the method of local spatial statistics describing the spatial correlation, the data field method was applied to the spatial structural feature extraction of high resolution images, and the extracted features can be used to improve image classification. Finally, the data field method was used to describe the spatial structure of residential areas, based on which this paper proposed a novel method of residential area detection form high resolution images. This method regarded buildings or their partial structures (such as feature points, feature lines) in residential areas as mass points, and used potential functions of data field to describe the internal structure of residential areas; Then, according to potential value differences between the residential and non-residential areas, residential areas can be extraced from backgrounds by threshold segmentation, and the detected residential areas can be further improved by some post-processings such as "noise" removal, hole filling.
本文的主要研究工作如下：
(1)对高分影像空间结构特征的概念、内涵和特点进行了探讨。借鉴系统科学对结构的认识,本文从影像的组成“元素”以及这些元素之间的“空间关系”来认识影像的空间结构。这里的“元素”是结构分析的基本单元,它既与影像自身的空间分辨率有关,也与人们对事物的认知模式密切相关。考虑到空间结构的多尺度特性,本文提出了高分影像空间结构特征的一个多层次分析框架,该框架将高分影像的空间结构划分为像元结构、目标结构和场景结构三个不同的层次。然后在该框架下,重点了研究高分影像在前两个层次的空间结构特征,并分别研究了基于空间自相关统计、空间半变差函数和数据场的空间结构特征建模方法。
(2)研究了基于空间自相关统计的高分影像空间结构描述方法。首先分析了空间自相关统计量对高分影像中不同地物空间依赖的响应。实验发现：Moran'sⅠ和Getis统计量对影像中的同质性区域响应较好,并且后者还能区分是高亮同质区还是低暗同质区；Geary's C统计量对影像中的异质性区域或边缘区域响应较好。进一步,根据影像中不同区域空间关联强度的差异,使用局部空间统计量和3D阈值分割法实现了高分影像中同质区和边缘结构的提取。接下来,本文将六个空间自相关统计量(包括三个全局统计量和三个局部空间统计量)用于高分影像局部空间结构的描述,并将提取的空间结构特征与光谱特征融合用于高分影像的分类,通过比较分类结果对各统计量的局部空间结构的描述能力进行了比较分析。实验发现：Moran's I和Getis统计量在改善影像中同质性地物的分类效果较好,同时Getis统计量还具有一定的平滑滤波的作用；Geary's C统计量能够显著改善影像中的异质性较大的地物类(如建筑区和植被)的分类结果；在相同的邻域窗口条件下,全局统计量能够比局部统计量获取更多的空间结构信息,但是其计算量会显著增大。最后,针对传统的空间自相关统计量描述高分影像局部空间结构信息的不足,本文提出了基于方向空间关联性的特征描述方法。该方法利用了方向射线,能够同时考虑空间关联的强度信息和长度信息,与通常的基于窗口的描述方法相比,它不仅能够提取高分影像局部的空间变异或表面纹理信息,还能够利用地物类的形状和结构信息,同时它还能够减少计算代价。与传统的基于窗口的Moran's I、Geary's C、灰度共生矩阵和小波变换方法进行比较,本文提出的方法都表现出了较优越的性能。
(3)研究了基于空间半变差函数的高分影像空间结构特征建模与居民区提取方法。本文将半变差函数的形态分为“平衡型”、“不平衡型”和“周期型”三种类型,并且给出了描述半变差函数曲线形态的多个参数,这些参数可用于描述高分影像的空间结构特征。进一步,本文提出了一种基于半变差的高分影像居民区检测方法。该方法利用半变差函数来建模居民区的空间结构特征,实验发现,高分影像中居民区的半变差函数符合具有一定周期的孔穴效应模型。基于该模型本文提取了表征居民区结构的特征参数,并利用该特征通过对高分影像进行基于分块的分类实现居民区的检测。
(4)研究了基于数据场的高分影像空间结构建模与居民区提取方法。首先比较了数据场方法与空间统计学中局部空间统计量之间的关系,本文获得了以下结论：通过选择合适的空间权重,局部空间统计量可以统一到数据场势函数体系,或者说局部空间统计量可以作为数据场描述数据对象相互作用的一种势函数。然后借鉴局部空间统计量描述空间关联性的方法,将数据场方法用于高分影像局部空间结构特征的提取,提取的新特征改善了高分影像的分类。最后,将数据场方法用于居民区空间结构的描述与建模,提出了一种基于数据场的高分影像居民区检测方法。该方法将居民区中的建筑物或其部分特征(如特征点、特征线)看成是质点,居民区内部的空间结构通过场来进行描述和建模；进一步,根据居民区与非居民区在势值上的差异,通过阈值分割的方式实现居民区的分割,并通过“噪声”去除、孔洞填充等方法完善了居民区的提取结果。
High-resolution earth observation technology has obtained rapid development over the past decade, and at present, it has become one of the frontiers of high-tech development in the world. With a spatial resolution of meter or sub-meter level, high-resolution images can provide more detailed ground information and how to achieve the image interpretation and information extraction has become an important research topic. Spatial structural feature is one of the most significant spatia features of high resolution images, in which most of the object types exhibit different degrees of structural characteristics such as textural structure, geometrc structure and spatial relationships. It is well known that effectively utilizing spatial structural information can play an important role in impoving the image classfication result and increasing the object detection accuracy. In this paper, we studied the methods of spatial structural feature description, modeling and extraction based on spatial statistics and data field. The structural features or structural model obtained by the proposed methods were then applied to improving the image classification or achieving the detection of residential area class.
The main work of this paper are as follows:
(1) Discussed the concept, connotation and characteristics of spatial structural features in high resolution images. Drawing on the theory of system science which includes the cognition and understanding of structure, we recognized the spatial structure of an image from two aspects:one is the composed "element" of the image, and the other is the "spatial structural relationship" among these elements. Here the "element" is the basic unit of structure analysis, which is closely related to the spatial resolution of the image itself as well as human cognitive pattern of things. Taking into account the multi-scale characteristics of spatial structure, this paper proposed a multi-level analysis framework of spatial structure, which divided the spatial structure of high resolution images into three different levels including pixel structure, object structure and scene structure. In this framework, this paper focused on the first two levels of spatial structure, and studied the methods of spatial structural feature modeling based on spatial autocorrelation statistics, semi-variance function and data field, respectively.
(2)Studied the methods of spatial structural feature description based on spatial autocorrelation statistics. First, we tested the response characteristics of spatial autocorrelation statistics to spatial dependance of different object classes in high resolution images. It was found that, Moran's I and Getis statistics can make a good response to homogeneous regions in an image, and the latter can distinguish between homogeneous regions of high value and low value; Geary's C statistic can make a strong response to heterogeneous regions or edges in the image. Further, according to intensity differences of spatial correlation in different regions in the image, this paper proposed a novel method of extracting homogeneous regions and edge structure from images using local spatial statistics. Second, the well-known six spatial autocorrelation statistics (including three global statistics and three local spatial statistics) were applied to the local spatial structural feature description and extraction of high resolution image, and the extracted spatial features were integrated with the spectral features for image classification. By comparing the classification results of using each statistic, the performance of each statistic was evaluated. Experimental results indicate that Moran's I and Getis statistics can better improve the classification of homogeneous object classes, while Geary's C statistic can significantly improve the classification of heterogeneous object classes (such as built-up areas and vegetation). It was also found that within the same neighborhood window, global statistics can get more spatial structural information but also need more computation cost than local statistics. Finally, to overcome some shortcomings of spatial autocorrelation statistics in the local spatial structure description, this paper proposed a novel method of modeling spatial structural features using directional spatial correlation(DSC). This method used eight directional half-lines instead of a window to measure spatial correlation in neighborhoods of pixels, which can take into account both the intensity and the length of spatial correlation. Compared with the usual window-ased methods, the proposed method can not only extract the information of spatial variability or surface texture but also can use the geometric information about shape and structure of object classes. In particular, it can also reduce the computation cost. By comparison, it was found the proposed method outperform some existing algorithms including Moran's I, Geary's C, GLCM, and showed competitive performance against wavelet transform based methods in accuracy or computation time.
(3) Studied the methods of spatial structural feature modeling and residential area extraction based on based on semi-variance function. This paper first divided the common semi-variograms into three different types:"balanced","unbalanced" and "periodic", and then studied the spatial structural feature extraction method based on parameters characterizing semi-variogram curve shape. Then, a novel method of residential areas detection based on semi-variance function was proposed. This method used semi-variance function to modeling the spatial structure of residential areas, and it was found by experiments that the semi-variogram of residential areas is subject to the hole effect model with a certain period. Based on this model, this paper extracted feature parameters characterizing the spatial structure of residential areas, and further the extracted features were applied to the detection of residential areas.
(4) Studied the methods of spatial structural feature modeling and residential area extraction based data field. First, this paper studied the relationship between the local spatial statistics in spatial statistics and the data field, and found that by selecting appropriate spatial weights, local spatial statistics can be unified into the potential function system of data field. In other words, local spatial statistics can be used as a potential function of data field, describing the interaction between data objects. Second, drawing on the method of local spatial statistics describing the spatial correlation, the data field method was applied to the spatial structural feature extraction of high resolution images, and the extracted features can be used to improve image classification. Finally, the data field method was used to describe the spatial structure of residential areas, based on which this paper proposed a novel method of residential area detection form high resolution images. This method regarded buildings or their partial structures (such as feature points, feature lines) in residential areas as mass points, and used potential functions of data field to describe the internal structure of residential areas; Then, according to potential value differences between the residential and non-residential areas, residential areas can be extraced from backgrounds by threshold segmentation, and the detected residential areas can be further improved by some post-processings such as "noise" removal, hole filling.
引文
[1]埃米尔·迪尔凯姆著,胡伟译.社会学方法的规则[M].北京：华夏出版社,1999.
[2]安如,冯学智,王慧麟.基于数学形态学的道路遥感影像特征提取及网络分析[J].中国图象图形学报,2003,8(7)：798-804.
[3]陈衫,秦其明.基于小波变换的高分辨率影像纹理结构分类方法[J].地理与地理信息科学,2003,19(3)：6-9.
[4]程承旗,喻新,郭仕德等.城市居住单元建筑拥挤度环境遥感分析——以厦门市为例[J].北京大学学报(自然科学版),2005,41(6)：875-881.
[5]崔卫红.基于图论的面向对象的高分辨率影像分割方法研究[D].武汉：武汉大学,2010.
[6]崔屹.图像处理与分析——数学形态学方法及应用[M].北京：科学出版社,2000.
[7]淦文燕,李德毅,王建民.一种基于数据场的层次聚类方法[J].电子学报,2006,34(2)：258-262.
[8]淦文燕,赫南,李德毅等.一种基于拓扑势的网络社区发现方法[J].软件学报,2009,20(8)：2241-2254.
[9]冈萨雷斯等著,阮秋琦译.数字图像处理(MATLAB版)[M].北京：电子工业出版社,2008.
[10]谷多玉,郭江,李书晓等.基于Gabor滤波器的航空图像居民区域提取.北京航空航天大学学报,2012,38(1)：106-110.
[11]顾娟,陈军,张宏伟等.基于遥感影像的居民地提取研究综述与展望[J].计算机工程与应用,2007,43(30)：1-10.
[12]库尔特·勒温.拓扑心理学原理[M].浙江：浙江教育出版社,1997.
[13]李德仁,童庆禧,李荣兴等.高分辨率对地观测的若干前沿科学问题[J].中国科学：地球科学,2012,42(6)：805-813.
[14]李德毅,杜鹢.不确定性人工智能[M].北京：国防工业出版社,2005.
[15]林宗坚,李德仁,胥燕婴.对地观测技术最新进展评述[J].测绘科学,2011,36(4)：5-8.
[16]刘耀林,唐旭,何建华.基于数据场的空间分析技术及其在土地定级中的应用[J].武汉大学学报(信息科学版),2009,34(9)：1009-1013.
[17]罗四维.视觉感知系统信息处理理论[M].北京：电子工业出版社,2006.
[18]罗四维.视觉信息认知计算理论[M].北京：科学出版社,2010.
[19]马荣华,蒲英霞,马晓东.GIS空间关联模式发现[M].北京：科学出版社,2007.
[20]潘励,王华,张剑清.遥感影像居民地目标Bayesian网络识别算法研究[J].武汉大学学报(信息科学版),2007,32(12)：1103-1106.
[21]秦昆.GIS空间分析理论与方法[M].武汉：武汉大学出版社,2010.
[22]秦昆,陈一祥,甘顺子等.高分辨率遥感影像空间结构特征建模方法综述[J].中国图象图形学报,2013,18(9)：1055-1064.
[23]四维科技,胡小锋,赵辉Visual C++/MATLAB图像处理与识别实用案例精选[M].北京：人民邮电出版社,2004.
[24]孙显,付琨,王宏琦.高分辨率遥感图像理解[M].北京：科学出版社,2011.
[25]陶超.高分辨率遥感影像中的城区与建筑物检测方法研究[D].武汉：华中科技大学,2012.
[26]陶建斌,舒宁,沈照庆.基于数据场聚类的遥感影像分类方法研究[J].国土资源遥感,2008,3：20-26.
[27]陶阳宇,梁林孙,徐迎庆等.利用贝叶斯结构模型的复杂形状建筑物提取[J].计算机辅 助设计与图形学学报,2010,22(4)：547-653.
[28]万玮,冯学智,肖鹏峰等.基于傅里叶描述子的高分辨率遥感图像地物形状特征表达[J].遥感学报,2011,15(1)：73-87.
[29]王华,潘励.基于纹理边缘与感知编组的居民地外轮廓提取[J].武汉大学学报(信息科学版),2010,35(1)：114-117.
[30]王树良.基于数据场与云模型的空间数据挖掘和知识发现[D].武汉：武汉大学,2002.
[31]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.
[32]吴涛.图像分割的认知物理学方法研究[D].武汉：武汉大学,2012.
[33]肖鹏峰,冯学智.高分辨率遥感图像分割与信息提取[M].北京：科学出版社,2012
[34]余长慧,易尧华.利用MRF方法的高分辨率影像道路提取[J].武汉大学学报(信息科学版),2011,36(5)：544-547.
[35]张小军.社会场[J].社会科学研究,1990,4：36-43.
[36]朱钟正,苏伟.基于局部空间统计分析的SPOT5影像分类[J].遥感学报,2011,15(5)：965-972.
[37]Akcay H G, Aksoy S. Automatic Detection of Geospatial Objects Using Multiple Hierarchical Segmentations[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(7), pp.2097-2111.
[38]Aksoy S, Koperski K, Tusk C, et al. Learning Bayesian classifiers for scene classification with a visual grammar [J]. IEEE Transactions on Geoscience and Remote Sensing,2005, 43(3):581-589.
[39]Aldstadt J, Getis A. Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis,2004,36(2):90-104.
[40]Aldstadt J, Getis A. Using AMOEBA to Creat a Spatial Weights Matrix and Identify Spatial Clusters. Geographical Analysis,2006,38(4):327-343.
[41]Anselin L. Local Indicators of Spatial Association-LISA [J]. Geographical Analysis,1995, 27(2):93-115.
[42]Aytekin O, Ulusoy I. Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology [J]. Pattern Recognition Letters,2011, 32(13):1618-1625.
[43]Balaguer A, Ruiz L A, Hermosilla T, et al. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification [J]. Computers & Geosciences,2010,36(2):231-240.
[44]Baraldi A, Parmiggiani F. An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters [J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33 (2):293-304.
[45]Bellens R, Gautama S, Martinez-Fonte L, et al. Improved classification of VHR images of urban areas using directional morphological profiles [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46 (10):2803-2812.
[46]Benediktsson J A, Pesaresi M, Arnason K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(9):1940-1949.
[47]Benediktsson J A, Swain P H, Ersoy O K. Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data[J]. IEEE Transactions on Geoscience and Remote Sensing.1990,28(4):540-552.
[48]Benz U, Hofinann P, Willhauck G, et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRS Journal of Photogrammetry & RemoteSensing,2004,58(3-4):239-258.
[49]Besag J. Spatial interactions and the statistical analysis of lattice systems (with discussion) [J]. Journal of Royal Statistician Society,1974,36(2):192-236.
[50]Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms [M], New York: Plenum Press.1981.
[51]Bhagavathy S, Manjunath B S. Modeling and detection of geospatial objects using texture motifs [J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(12):3706-3715.
[52]Blaschke T. Object based image analysis for remote sensing [J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.
[53]Boyer K L, Sarkar S. Perceptual organization in computer vision:status, challenges, and potential [J]. Computer Vision and Image Understanding,1999,76 (1):1-5.
[54]Breiman L. Random Forests [J]. Machine Learning,2001,45(1):5-32.
[55]Bruzzone L, Carlin L. A multilevel context-based system for classification of very high spatial resolution images [J]. IEEE Transactions on Geoscience and Remote Sensing,2006, 44(9):2587-2599.
[56]Chang C C, Lin C J. LIBSVM:a library for support vector machines,2001. [Online]. Available:http://www.csie.ntu.edu.tw/-cjlin/libsvm.
[57]Chen Q, Gong P. Automatic variogram parameter extraction for texture classification of the panchromatic IKONOS imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(5):1106-1115.
[58]Chen Y X, Qin K, Liu Y, et al. Feature modelling of high resolution remote sensing images considering spatial autocorrelation[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3,2012.
[59]Dogrusoz E, Aksoy S. Modeling urban structures using graph-based spatial patterns [J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2007,4826-4829.
[60]Foody G M, Mathur A. A relative evaluation of multiclass image classification by support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(6): 1335-1343.
[61]Franklin S E, Wulder M A, Lavigne M B. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis [J]. Computers & Geosciences,1996, 22(6):665-673.
[62]GetisA. Spatial weight matrices. Geographical Analysis,2009,41(4):404-410.
[63]Getis A, Ord J K. The Analysis of Spatial Association by Use of Distance Statistics [J]. Geographical Analysis,1992,24(3):189-206
[64]Ghimire B, Rogan J, Miller J. Contextual land-cover classification:incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic[J]. Remote Sensing Letters,2010,1(1):45-54.
[65]Griffith D A. Spatial statistics:A quantitative geographer's perspective [J]. Spatial Statistics, 2012,1:3-15.
[66]Han F, Zhu S C. Bottom-up/top-down image parsing by attribute graph grammar [C].// Proceedings of the Tenth IEEE International Conference on Computer Vision. Washington, DC, USA:IEEE Computer Society,2005,2:1778-1785.
[67]Haralick R M, Shanmugan K, Dinstein I. Textural features for image classification [J]. IEEE Trans. Syst. Man Cybern., vol. SMC-3, no.6, pp.610-621, Nov.1973.
[68]Harris C, Stephens. A combined corner and edge detector. Proceedings of the 4th alvey vision conference,1988,147-151.
[69]He C, Xia G S, Sun H. An adaptive and iterative method of urban area extraction from SAR images [J]. IEEE Geoscience and Remote Sensing Letters,2006,3(4):504-392.
[70]Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification,2008. [Online]. Available:http://www.csie.ntu.edu.tw/-cjnn/papers/guide/guide.pdf.
[71]Hu X Y, Shen J J, Shan J, et al. Local Edge Distributions for Detection of Salient Structure Textures and Objects [J]. IEEE Geoscience and Remote Sensing Letters,2013,10(3): 466-470.
[72]Hu X Y, Tao V. Automatic extraction of main road centerlines from high resolution satellite imagery using hierarchical grouping [J]. Photogrammetric Engineering & Remote Sensing, 2007,73(9):1049-1056.
[73]Huang C, Davis L S, Townshend J R G. An Assessment of Support Vector Machines for Land Cover Classification [J]. International Journal of Remote Sensing,2002,23(4):725-749.
[74]Huang X, Zhang L P, Li P X. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,4(2):260-264.
[75]Huang X, Zhang L P, Li P X. A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform [J]. International Journal of Remote Sensing,2008,29(20):5923-5941.
[76]Iqbal Q, Aggarwal J K. Retrieval by classification of images containing large manmade objects using perceptual grouping [J]. Pattern Recognition,2002,35(7):1463-1479.
[77]Johnson B, Xie Z. Unsupervised image segmentation evaluation and refinement using a multi-scale approach [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2011,66(4): 473-483.
[78]Karathanassi V, Iossifidis C, Rokos D. A texture-based classification method for classifying built areas according to their density [J]. International Journal of Remote Sensing,2000, 21(9):1807-1823.
[79]Katartzis A, Sahli H. A stochastic framework for the identification of building rooftops using a single remote sensing image [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(1):259-271.
[80]Kavzoglu T, Colkesen I. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 2009,11(5):352-359.
[81]Kim T, Muller J P. Development of a graph-based approach for building detection [J]. Image and Vision Computing,1999,17(1):3-14.
[82]Korting T S, Dutra L V, Fonseca G L M. A resegmentation approach for detecting rectangular objects in high-resolution imagery [J]. IEEE Geoscience and Remote Sensing Letters,2011, 8(4):621-625.
[83]Lafarge F, Descombes X, Zerubia J, et al. Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2008,63 (3):365-381.
[84]Li J, Narayanan R M. Integrated Spectral and Spatial Information Mining in Remote Sensing Imagery [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):673-685.
[85]Li S Z. Markov Random Field Modeling in Image Analysis [M]. NewYork:Springer-Verlag, 2001.
[86]Liu Y, Guo Q H, Kelly M. A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2008,63:461-475.
[87]Lorette A. Texture analysis through a markovian modeling and fuzzy classification: application to urban area extraction from satellite images [J]. International Journal of Computer Vision,2000,36(3):221-236.
[88]Mallinis G, Koutsias N, Tsakiri-Strati M, and Karteris M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site [J]. ISPRS Journal of Photogrammetry & Remote Sens,2008,63(2):237-250.
[89]Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data [J]. IEEE Transactions on. Pattern Analysis and Machine Intelligence,1996,18:837-842.
[90]Martinez-Fonte L, Gautama S, Philips W, et al. Evaluating Corner Detectors for the Extraction of Man-made Structures in Urban Areas [C]. Proc. IEEE Intern. Geoscience and Remote Sensing Symposium (IGARSS),2005.
[91]Meer F V D. Remote sensing image analysis and geostatistics [J]. International Journal of Remote Sensing,2012,33(18):5644-5676.
[92]Melgani F, Bruzzone L. Classification of Hyperspectral Remote Sensing Images with Support Vector Machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8): 1778-1790.
[93]Mohammadzadeh A, Tavakoli A, Valadan Zoej M J. Road extraction based on fuzzy logic and mathematical morphology from pan-sharpened IKONOS images [J]. The Photogrammetric Record,2006,21(113):44-60.
[94]Mountrakis G, Ira J, Ogole C. Support vector machines in remote sensing:A review [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2011,66:247-259.
[95]Myint S W, Lam N S, Tyler J M. Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches[J]. Photogrammetric Engineering & Remote Sensing,2004,70(7):803-812.
[96]Myint S W, Wentz E A, Purkis S J. Employing spatial metrics in urban land-use/land-cover mapping:comparing the Getis and Geary indices [J]. Photogrammetric Engineering & Remote Sensing,2007,73(12):1403-1415.
[97]Ord J K, Getis A. Local Spatial Autocorrelation Statistics:Distributional Issues and an Application[J]. Geographical Analysis,1995,27(4):286-306
[98]Ortner M, Descombes X, Zerubia J. Building outline extraction from digital elevation models using marked point processes [J]. International Journal of Computer Vision,2007,72(2): 107-132.
[99]Otsu N A. Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics,1979,9(1):62-66.
[100]Ouma Y O, Ngigi T G, Tateishi R. On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation [J]. International Journal of Remote Sensing,2006,27(1):73-104.
[101]Ouma Y O, Tetuko J, Tateishi R. Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery [J]. International Journal of Remote Sensing,2008,29(12):3417-3456.
[102]Pacifici F, Chini M, Emery W J. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification [J]. Remote Sensing of Environment,2009,113(6):1276-1292.
[103]Pal M, Mather P M. Support Vector Machines for Classification in Remote Sensing [J]. International Journal of Remote Sensing,2005,26(5):1007-1011.
[104]Park I K, Lee K M, Lee S U. Perceptual grouping of line features in 3-D space:a model-based framework [J]. Pattern Recognition,2004,37(1):145-159.
[105]Pesaresi M, Benediktsson J A. A new approach for the morphological segmentation of high-resolution satellite imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39 (2),309-320.
[106]Pesaresi M, Gerhardinger A, Kayitakire F. A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(3):180-192.
[107]Porway J, Wang Q C, Zhu S C. A hierarchical and contextual model for aerial image parsing [J]. International Journal of Computer Vision,2010,88(2):254-283.
[108]Puissant A, Hirsch J, Weber C. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery[J]. International Journal of Remote Sensing,2005,26(4):733-745.
[109]Rodriguez-Galiano V F, Chica-Olmo M, Abarca-Hernandez F, et al. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture [J]. Remote Sensing of Environment,2012,121:93-107.
[110]Sarkar S, Boyer K L. Perceptual organization in computer vision:a review and a proposal for a classifacatory structure [J]. IEEE Trans. System Man Cybernet,1993,23 (2):382-399.
[111]Shi J, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[112]Sirmacek B, Unsalan C. Urban-area and building detection using SIFT keypoints and graph theory [J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(4):1156-1167.
[113]Sirmacek B, Unsalan C. Urban Area Detection Using Local Feature Points and Spatial Voting. IEEE Geoscience and Remote Sensing Letters,2010,7(1):146-150.
[114]Stasolla M, Gamba P. Spatial Indexes for the Extraction of Formal and Informal Human Settlements From High-Resolution SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(2):98-106.
[115]Stein A, Bastiaanssen W G M, Bruin S D, et al. Integrating spatial statistics and remote sensing [J]. International Journal of Remote Sensing,1998,19(9):1793-1814.
[116]Su W, Zhang C, Yang J Y, et al. Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery [J]. International Journal of Remote Sensing,2012,33(11):3557-3579.
[117]Su W, Li J, Chen Y, et al. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery [J]. International Journal of Remote Sensing,2008,29(11):3105-3117.
[118]Sun G Y, Liu Q H, Liu Q, et al. A novel approach for edge detection based on the theory of universal gravity[J]. Pattern Recognition,2007,40(10):2766-2775.
[119]Tobler W R. A computer movie simulating urban growth in the Detroit region. Economic Geogaphy,1970,46(2):234-240.
[120]Tso B, Olsen R C. A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process [J]. Remote Sensing of Environment, 2005,97(1):127-136.
[121]Unsalan C, Boyer K L. Classifying land development in high-resolution panchromatic satellite images using straight-line statistics [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):907-919.
[122]Vapnik V. Statistical Learning Theory [M]. New York:Wiley,1998.
[123]Weizman L, Goldberger J. Urban-Area Segmentation Using Visual Words. IEEE Geoscience and Remote Sensing Letters,2009,6(3):388-392.
[124]Wu S H, Xu B, Wang L. Urban land-use classification using variogram-based analysis with an aerial photograph [J]. Photogrammetric Engineering & Remote Sensing,2006,72(7): 813-822.
[125]Wu T, Qin K. Image data field for homogeneous region based segmentation[J]. Computers and Electrical Engineering,2012a,38(2):459-470.
[126]Wu T, Qin K. Data field-based transition region extraction and thresholding[J]. Optics and Lasers in Engineering,2012b,50(2):131-139.
[127]Wu T, Qin K. Data field-based mechanism for three-dimensional thresholding[J]. Neurocomputing,2012c,97:278-296.
[128]Wulder M, Boots B. Local Spatial Autocorrelation Characteristics of Remotely Sensed Imagery Assessed with the Getis Statistic [J]. International Journal of Remote Sensing,1998, 19(11):2223-2231
[129]Yue A Z, Zhang C, Yang J Y, et al. Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram[J]. International Journal of Remote Sensing,2013,34(11):3736-3759.
[130]Zhang Q, Zhang L P, Huang X. Classification of high-spatial resolution imagery based on distance-weighted markov random field with an improved iterated conditional mode method [J]. International Journal of Remote Sensing,2011,32(24):9843-9868.
[131]Zhang L, Huang X, Huang B, et al. A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(10):2950-2961.
[132]Zhong P, Wang R S. Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1469-1482.
[133]Zhong P, Wang R S. A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(12):3978-3988.
[134]Zhu C, Shi W Z, Pesaresi M, et al. The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics [J]. International Journal of Remote Sensing,2005,26(24):5493-5508.
[135]Zlatoff N, Tellez B, Baskurt A. Combining local belief from low-level primitives for perceptual grouping [J]. Pattern Recognition,2008,41(4):1215-1229.
[2]安如,冯学智,王慧麟.基于数学形态学的道路遥感影像特征提取及网络分析[J].中国图象图形学报,2003,8(7)：798-804.
[3]陈衫,秦其明.基于小波变换的高分辨率影像纹理结构分类方法[J].地理与地理信息科学,2003,19(3)：6-9.
[4]程承旗,喻新,郭仕德等.城市居住单元建筑拥挤度环境遥感分析——以厦门市为例[J].北京大学学报(自然科学版),2005,41(6)：875-881.
[5]崔卫红.基于图论的面向对象的高分辨率影像分割方法研究[D].武汉：武汉大学,2010.
[6]崔屹.图像处理与分析——数学形态学方法及应用[M].北京：科学出版社,2000.
[7]淦文燕,李德毅,王建民.一种基于数据场的层次聚类方法[J].电子学报,2006,34(2)：258-262.
[8]淦文燕,赫南,李德毅等.一种基于拓扑势的网络社区发现方法[J].软件学报,2009,20(8)：2241-2254.
[9]冈萨雷斯等著,阮秋琦译.数字图像处理(MATLAB版)[M].北京：电子工业出版社,2008.
[10]谷多玉,郭江,李书晓等.基于Gabor滤波器的航空图像居民区域提取.北京航空航天大学学报,2012,38(1)：106-110.
[11]顾娟,陈军,张宏伟等.基于遥感影像的居民地提取研究综述与展望[J].计算机工程与应用,2007,43(30)：1-10.
[12]库尔特·勒温.拓扑心理学原理[M].浙江：浙江教育出版社,1997.
[13]李德仁,童庆禧,李荣兴等.高分辨率对地观测的若干前沿科学问题[J].中国科学：地球科学,2012,42(6)：805-813.
[14]李德毅,杜鹢.不确定性人工智能[M].北京：国防工业出版社,2005.
[15]林宗坚,李德仁,胥燕婴.对地观测技术最新进展评述[J].测绘科学,2011,36(4)：5-8.
[16]刘耀林,唐旭,何建华.基于数据场的空间分析技术及其在土地定级中的应用[J].武汉大学学报(信息科学版),2009,34(9)：1009-1013.
[17]罗四维.视觉感知系统信息处理理论[M].北京：电子工业出版社,2006.
[18]罗四维.视觉信息认知计算理论[M].北京：科学出版社,2010.
[19]马荣华,蒲英霞,马晓东.GIS空间关联模式发现[M].北京：科学出版社,2007.
[20]潘励,王华,张剑清.遥感影像居民地目标Bayesian网络识别算法研究[J].武汉大学学报(信息科学版),2007,32(12)：1103-1106.
[21]秦昆.GIS空间分析理论与方法[M].武汉：武汉大学出版社,2010.
[22]秦昆,陈一祥,甘顺子等.高分辨率遥感影像空间结构特征建模方法综述[J].中国图象图形学报,2013,18(9)：1055-1064.
[23]四维科技,胡小锋,赵辉Visual C++/MATLAB图像处理与识别实用案例精选[M].北京：人民邮电出版社,2004.
[24]孙显,付琨,王宏琦.高分辨率遥感图像理解[M].北京：科学出版社,2011.
[25]陶超.高分辨率遥感影像中的城区与建筑物检测方法研究[D].武汉：华中科技大学,2012.
[26]陶建斌,舒宁,沈照庆.基于数据场聚类的遥感影像分类方法研究[J].国土资源遥感,2008,3：20-26.
[27]陶阳宇,梁林孙,徐迎庆等.利用贝叶斯结构模型的复杂形状建筑物提取[J].计算机辅 助设计与图形学学报,2010,22(4)：547-653.
[28]万玮,冯学智,肖鹏峰等.基于傅里叶描述子的高分辨率遥感图像地物形状特征表达[J].遥感学报,2011,15(1)：73-87.
[29]王华,潘励.基于纹理边缘与感知编组的居民地外轮廓提取[J].武汉大学学报(信息科学版),2010,35(1)：114-117.
[30]王树良.基于数据场与云模型的空间数据挖掘和知识发现[D].武汉：武汉大学,2002.
[31]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.
[32]吴涛.图像分割的认知物理学方法研究[D].武汉：武汉大学,2012.
[33]肖鹏峰,冯学智.高分辨率遥感图像分割与信息提取[M].北京：科学出版社,2012
[34]余长慧,易尧华.利用MRF方法的高分辨率影像道路提取[J].武汉大学学报(信息科学版),2011,36(5)：544-547.
[35]张小军.社会场[J].社会科学研究,1990,4：36-43.
[36]朱钟正,苏伟.基于局部空间统计分析的SPOT5影像分类[J].遥感学报,2011,15(5)：965-972.
[37]Akcay H G, Aksoy S. Automatic Detection of Geospatial Objects Using Multiple Hierarchical Segmentations[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(7), pp.2097-2111.
[38]Aksoy S, Koperski K, Tusk C, et al. Learning Bayesian classifiers for scene classification with a visual grammar [J]. IEEE Transactions on Geoscience and Remote Sensing,2005, 43(3):581-589.
[39]Aldstadt J, Getis A. Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis,2004,36(2):90-104.
[40]Aldstadt J, Getis A. Using AMOEBA to Creat a Spatial Weights Matrix and Identify Spatial Clusters. Geographical Analysis,2006,38(4):327-343.
[41]Anselin L. Local Indicators of Spatial Association-LISA [J]. Geographical Analysis,1995, 27(2):93-115.
[42]Aytekin O, Ulusoy I. Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology [J]. Pattern Recognition Letters,2011, 32(13):1618-1625.
[43]Balaguer A, Ruiz L A, Hermosilla T, et al. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification [J]. Computers & Geosciences,2010,36(2):231-240.
[44]Baraldi A, Parmiggiani F. An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters [J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33 (2):293-304.
[45]Bellens R, Gautama S, Martinez-Fonte L, et al. Improved classification of VHR images of urban areas using directional morphological profiles [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46 (10):2803-2812.
[46]Benediktsson J A, Pesaresi M, Arnason K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(9):1940-1949.
[47]Benediktsson J A, Swain P H, Ersoy O K. Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data[J]. IEEE Transactions on Geoscience and Remote Sensing.1990,28(4):540-552.
[48]Benz U, Hofinann P, Willhauck G, et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRS Journal of Photogrammetry & RemoteSensing,2004,58(3-4):239-258.
[49]Besag J. Spatial interactions and the statistical analysis of lattice systems (with discussion) [J]. Journal of Royal Statistician Society,1974,36(2):192-236.
[50]Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms [M], New York: Plenum Press.1981.
[51]Bhagavathy S, Manjunath B S. Modeling and detection of geospatial objects using texture motifs [J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(12):3706-3715.
[52]Blaschke T. Object based image analysis for remote sensing [J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.
[53]Boyer K L, Sarkar S. Perceptual organization in computer vision:status, challenges, and potential [J]. Computer Vision and Image Understanding,1999,76 (1):1-5.
[54]Breiman L. Random Forests [J]. Machine Learning,2001,45(1):5-32.
[55]Bruzzone L, Carlin L. A multilevel context-based system for classification of very high spatial resolution images [J]. IEEE Transactions on Geoscience and Remote Sensing,2006, 44(9):2587-2599.
[56]Chang C C, Lin C J. LIBSVM:a library for support vector machines,2001. [Online]. Available:http://www.csie.ntu.edu.tw/-cjlin/libsvm.
[57]Chen Q, Gong P. Automatic variogram parameter extraction for texture classification of the panchromatic IKONOS imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(5):1106-1115.
[58]Chen Y X, Qin K, Liu Y, et al. Feature modelling of high resolution remote sensing images considering spatial autocorrelation[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3,2012.
[59]Dogrusoz E, Aksoy S. Modeling urban structures using graph-based spatial patterns [J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2007,4826-4829.
[60]Foody G M, Mathur A. A relative evaluation of multiclass image classification by support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(6): 1335-1343.
[61]Franklin S E, Wulder M A, Lavigne M B. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis [J]. Computers & Geosciences,1996, 22(6):665-673.
[62]GetisA. Spatial weight matrices. Geographical Analysis,2009,41(4):404-410.
[63]Getis A, Ord J K. The Analysis of Spatial Association by Use of Distance Statistics [J]. Geographical Analysis,1992,24(3):189-206
[64]Ghimire B, Rogan J, Miller J. Contextual land-cover classification:incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic[J]. Remote Sensing Letters,2010,1(1):45-54.
[65]Griffith D A. Spatial statistics:A quantitative geographer's perspective [J]. Spatial Statistics, 2012,1:3-15.
[66]Han F, Zhu S C. Bottom-up/top-down image parsing by attribute graph grammar [C].// Proceedings of the Tenth IEEE International Conference on Computer Vision. Washington, DC, USA:IEEE Computer Society,2005,2:1778-1785.
[67]Haralick R M, Shanmugan K, Dinstein I. Textural features for image classification [J]. IEEE Trans. Syst. Man Cybern., vol. SMC-3, no.6, pp.610-621, Nov.1973.
[68]Harris C, Stephens. A combined corner and edge detector. Proceedings of the 4th alvey vision conference,1988,147-151.
[69]He C, Xia G S, Sun H. An adaptive and iterative method of urban area extraction from SAR images [J]. IEEE Geoscience and Remote Sensing Letters,2006,3(4):504-392.
[70]Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification,2008. [Online]. Available:http://www.csie.ntu.edu.tw/-cjnn/papers/guide/guide.pdf.
[71]Hu X Y, Shen J J, Shan J, et al. Local Edge Distributions for Detection of Salient Structure Textures and Objects [J]. IEEE Geoscience and Remote Sensing Letters,2013,10(3): 466-470.
[72]Hu X Y, Tao V. Automatic extraction of main road centerlines from high resolution satellite imagery using hierarchical grouping [J]. Photogrammetric Engineering & Remote Sensing, 2007,73(9):1049-1056.
[73]Huang C, Davis L S, Townshend J R G. An Assessment of Support Vector Machines for Land Cover Classification [J]. International Journal of Remote Sensing,2002,23(4):725-749.
[74]Huang X, Zhang L P, Li P X. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,4(2):260-264.
[75]Huang X, Zhang L P, Li P X. A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform [J]. International Journal of Remote Sensing,2008,29(20):5923-5941.
[76]Iqbal Q, Aggarwal J K. Retrieval by classification of images containing large manmade objects using perceptual grouping [J]. Pattern Recognition,2002,35(7):1463-1479.
[77]Johnson B, Xie Z. Unsupervised image segmentation evaluation and refinement using a multi-scale approach [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2011,66(4): 473-483.
[78]Karathanassi V, Iossifidis C, Rokos D. A texture-based classification method for classifying built areas according to their density [J]. International Journal of Remote Sensing,2000, 21(9):1807-1823.
[79]Katartzis A, Sahli H. A stochastic framework for the identification of building rooftops using a single remote sensing image [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(1):259-271.
[80]Kavzoglu T, Colkesen I. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 2009,11(5):352-359.
[81]Kim T, Muller J P. Development of a graph-based approach for building detection [J]. Image and Vision Computing,1999,17(1):3-14.
[82]Korting T S, Dutra L V, Fonseca G L M. A resegmentation approach for detecting rectangular objects in high-resolution imagery [J]. IEEE Geoscience and Remote Sensing Letters,2011, 8(4):621-625.
[83]Lafarge F, Descombes X, Zerubia J, et al. Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2008,63 (3):365-381.
[84]Li J, Narayanan R M. Integrated Spectral and Spatial Information Mining in Remote Sensing Imagery [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):673-685.
[85]Li S Z. Markov Random Field Modeling in Image Analysis [M]. NewYork:Springer-Verlag, 2001.
[86]Liu Y, Guo Q H, Kelly M. A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2008,63:461-475.
[87]Lorette A. Texture analysis through a markovian modeling and fuzzy classification: application to urban area extraction from satellite images [J]. International Journal of Computer Vision,2000,36(3):221-236.
[88]Mallinis G, Koutsias N, Tsakiri-Strati M, and Karteris M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site [J]. ISPRS Journal of Photogrammetry & Remote Sens,2008,63(2):237-250.
[89]Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data [J]. IEEE Transactions on. Pattern Analysis and Machine Intelligence,1996,18:837-842.
[90]Martinez-Fonte L, Gautama S, Philips W, et al. Evaluating Corner Detectors for the Extraction of Man-made Structures in Urban Areas [C]. Proc. IEEE Intern. Geoscience and Remote Sensing Symposium (IGARSS),2005.
[91]Meer F V D. Remote sensing image analysis and geostatistics [J]. International Journal of Remote Sensing,2012,33(18):5644-5676.
[92]Melgani F, Bruzzone L. Classification of Hyperspectral Remote Sensing Images with Support Vector Machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8): 1778-1790.
[93]Mohammadzadeh A, Tavakoli A, Valadan Zoej M J. Road extraction based on fuzzy logic and mathematical morphology from pan-sharpened IKONOS images [J]. The Photogrammetric Record,2006,21(113):44-60.
[94]Mountrakis G, Ira J, Ogole C. Support vector machines in remote sensing:A review [J]. ISPRS Journal of Photogrammetry & Remote Sensing,2011,66:247-259.
[95]Myint S W, Lam N S, Tyler J M. Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches[J]. Photogrammetric Engineering & Remote Sensing,2004,70(7):803-812.
[96]Myint S W, Wentz E A, Purkis S J. Employing spatial metrics in urban land-use/land-cover mapping:comparing the Getis and Geary indices [J]. Photogrammetric Engineering & Remote Sensing,2007,73(12):1403-1415.
[97]Ord J K, Getis A. Local Spatial Autocorrelation Statistics:Distributional Issues and an Application[J]. Geographical Analysis,1995,27(4):286-306
[98]Ortner M, Descombes X, Zerubia J. Building outline extraction from digital elevation models using marked point processes [J]. International Journal of Computer Vision,2007,72(2): 107-132.
[99]Otsu N A. Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics,1979,9(1):62-66.
[100]Ouma Y O, Ngigi T G, Tateishi R. On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation [J]. International Journal of Remote Sensing,2006,27(1):73-104.
[101]Ouma Y O, Tetuko J, Tateishi R. Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery [J]. International Journal of Remote Sensing,2008,29(12):3417-3456.
[102]Pacifici F, Chini M, Emery W J. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification [J]. Remote Sensing of Environment,2009,113(6):1276-1292.
[103]Pal M, Mather P M. Support Vector Machines for Classification in Remote Sensing [J]. International Journal of Remote Sensing,2005,26(5):1007-1011.
[104]Park I K, Lee K M, Lee S U. Perceptual grouping of line features in 3-D space:a model-based framework [J]. Pattern Recognition,2004,37(1):145-159.
[105]Pesaresi M, Benediktsson J A. A new approach for the morphological segmentation of high-resolution satellite imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39 (2),309-320.
[106]Pesaresi M, Gerhardinger A, Kayitakire F. A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(3):180-192.
[107]Porway J, Wang Q C, Zhu S C. A hierarchical and contextual model for aerial image parsing [J]. International Journal of Computer Vision,2010,88(2):254-283.
[108]Puissant A, Hirsch J, Weber C. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery[J]. International Journal of Remote Sensing,2005,26(4):733-745.
[109]Rodriguez-Galiano V F, Chica-Olmo M, Abarca-Hernandez F, et al. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture [J]. Remote Sensing of Environment,2012,121:93-107.
[110]Sarkar S, Boyer K L. Perceptual organization in computer vision:a review and a proposal for a classifacatory structure [J]. IEEE Trans. System Man Cybernet,1993,23 (2):382-399.
[111]Shi J, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[112]Sirmacek B, Unsalan C. Urban-area and building detection using SIFT keypoints and graph theory [J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(4):1156-1167.
[113]Sirmacek B, Unsalan C. Urban Area Detection Using Local Feature Points and Spatial Voting. IEEE Geoscience and Remote Sensing Letters,2010,7(1):146-150.
[114]Stasolla M, Gamba P. Spatial Indexes for the Extraction of Formal and Informal Human Settlements From High-Resolution SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(2):98-106.
[115]Stein A, Bastiaanssen W G M, Bruin S D, et al. Integrating spatial statistics and remote sensing [J]. International Journal of Remote Sensing,1998,19(9):1793-1814.
[116]Su W, Zhang C, Yang J Y, et al. Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery [J]. International Journal of Remote Sensing,2012,33(11):3557-3579.
[117]Su W, Li J, Chen Y, et al. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery [J]. International Journal of Remote Sensing,2008,29(11):3105-3117.
[118]Sun G Y, Liu Q H, Liu Q, et al. A novel approach for edge detection based on the theory of universal gravity[J]. Pattern Recognition,2007,40(10):2766-2775.
[119]Tobler W R. A computer movie simulating urban growth in the Detroit region. Economic Geogaphy,1970,46(2):234-240.
[120]Tso B, Olsen R C. A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process [J]. Remote Sensing of Environment, 2005,97(1):127-136.
[121]Unsalan C, Boyer K L. Classifying land development in high-resolution panchromatic satellite images using straight-line statistics [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):907-919.
[122]Vapnik V. Statistical Learning Theory [M]. New York:Wiley,1998.
[123]Weizman L, Goldberger J. Urban-Area Segmentation Using Visual Words. IEEE Geoscience and Remote Sensing Letters,2009,6(3):388-392.
[124]Wu S H, Xu B, Wang L. Urban land-use classification using variogram-based analysis with an aerial photograph [J]. Photogrammetric Engineering & Remote Sensing,2006,72(7): 813-822.
[125]Wu T, Qin K. Image data field for homogeneous region based segmentation[J]. Computers and Electrical Engineering,2012a,38(2):459-470.
[126]Wu T, Qin K. Data field-based transition region extraction and thresholding[J]. Optics and Lasers in Engineering,2012b,50(2):131-139.
[127]Wu T, Qin K. Data field-based mechanism for three-dimensional thresholding[J]. Neurocomputing,2012c,97:278-296.
[128]Wulder M, Boots B. Local Spatial Autocorrelation Characteristics of Remotely Sensed Imagery Assessed with the Getis Statistic [J]. International Journal of Remote Sensing,1998, 19(11):2223-2231
[129]Yue A Z, Zhang C, Yang J Y, et al. Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram[J]. International Journal of Remote Sensing,2013,34(11):3736-3759.
[130]Zhang Q, Zhang L P, Huang X. Classification of high-spatial resolution imagery based on distance-weighted markov random field with an improved iterated conditional mode method [J]. International Journal of Remote Sensing,2011,32(24):9843-9868.
[131]Zhang L, Huang X, Huang B, et al. A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(10):2950-2961.
[132]Zhong P, Wang R S. Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1469-1482.
[133]Zhong P, Wang R S. A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(12):3978-3988.
[134]Zhu C, Shi W Z, Pesaresi M, et al. The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics [J]. International Journal of Remote Sensing,2005,26(24):5493-5508.
[135]Zlatoff N, Tellez B, Baskurt A. Combining local belief from low-level primitives for perceptual grouping [J]. Pattern Recognition,2008,41(4):1215-1229.