基于结构模型的遥感图像军事阵地目标特征分析及其识别技术研究
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摘要
本文重点研究遥感图像中军事阵地的空间分布结构特征，以及阵地与其它目标的拓扑关系特征，其中的难点是研究导弹阵地类目标的空间分布结构特征。
首先分析了数据源的特点，确定使用模糊理论与图论分析军事阵地目标群中的子目标特性与目标阵列群的空间分布结构。
第二章使用Kubelka-Munk光照物理模型分析了可见光彩色信息中一些不受光照环境、视点位置、物体材质的菲涅耳反射系数影响的不变量；之后使用高斯积分分析了光谱能量分布结构，提出了与设备参数相关的彩色不变量实际求解方法。所求彩色不变量可作为特征，对遥感彩色图像进行粗分割。并提出一种基于特征量化的快速类数自适应模糊C均值分级聚类方法，用于快速提取子目标斑点。
第三章使用ARG模型描述军事阵地目标群，使用图论中的路径描述节点之间的多元关系R~m，拓展传统ARG模型为AR~mG模型，用于分析多个目标之间的多元关系与空间分布结构。之后采用多尺度的概念，提出了多层化的AR~mG模型，mAR~mG，用以分析不同尺度对象之间的关系。该模型具有G(G，E)的嵌套形式，节点不再是单独的节点，而可能是一个子图，子图在mAR~mG中的某一尺度上被作为独立节点来处理。之后将mAR~mG模型模糊化为模糊mA(?)~mG模型，为模糊信息融合提供信息源。
第四章分析了子目标斑点的几何形状特征。结合二元距离与相似关系，提出了目标阵列等间距分布关系的显著特征：目标之间的最小距离相等。之后针对Hu的矩不变量的尺度不变性进行了参数修正，使之适合于分析离散斑点集的整体空间分布形状。并提出可区分环状、团状、线状分布斑点集的重心距离特征与圆形度特征。之后对目前已有的模糊拓扑空间关系求解方法进行了比较研究。针对模糊拓扑环境传递算法进行了较深入的研究，提出了适合对大图像进行模糊拓扑空间关系求解的改进方法，提高了算法效率。最后提出了比以往定义更加符合人的直觉习惯的“毗邻”与“包围”模糊拓扑空间关系的计算式。
第五章提出一种新颖的基于模糊信息融合的可用来检测目标之间空间分布结构规则程度的路径空间关系约束支撑树算法，即最规则空间分布关系MSR(Max Spatial Regularity)检测算法。算法基于“约束”支撑树的思想，借助Prim算法搜寻最小支撑树的机制，评估出当前节点与已搜索节点，以及未搜索的邻近节点之间满足某种有规则的空间分布关系的满意程度，约束支撑树的生长过程，尽最大可能地主动探测子目标之间可能存在的规则的空间分布结构；最后得到一棵可用来评估目标群满足某种局部的规则空间分布关系满意程度的MSR支撑树；对MSR支撑树进行解模糊，推测目标群是否为目标阵列。算法具有良好的抗噪声性能，且计算量维持在O(N~2)的量级，N为子目标数目。
最后系统地总结了本文的研究工作，给出了进一步研究的建议和设想。
Military targets arrays' features, especially the spatial distribution features in remote sensing images are studied in this paper.Firstly, the characteristics of resource data are discussed. And then, the fuzzy theory and the graph theory are confirmed to be the main research theories of this paper.Some color invariant features which invariant with the lighting condition, the view point position, the Fresnel reflectance coefficient of object's material are studied based on the Kubelka-Munk physical illumination model in chapter 2. The spectrum energy distribution structure is studied by using Gaussian color model, and a color invariance calculation method from RGB component of images is proposed. The color invariant features are used for fast segmentation of colored remote sensing, or multi-spectral images. A fast cluster number adaptive fuzzy C mean algorithm based on feature quantitative is proposed for extracting the sub-target blob efficiently.The military targets array is customized by ARG model in chapter3. By investigating the "path" concept of graph theory, the traditional ARG model is extended to ARmG model, and a multi-side relationship system Rmof the nodes based on the path description is established. As a result, ARmG model is used for analyzing the spatial distribution relationship among targets by studying the shape of the path that connects tagets. A multi-layered ARmG (mARmG) model is proposed for analyzing the multi-scale structure of targets' relationships. The model has the form as G(G,E}, in which the node can be considered as a sub-graph. And then, the relationshipattributes of mARmG model are defined by fuzzy set. The mARmG model is extended to mARmG model, and becomes the data resources for fuzzy information integration.The geometric attributes of target blobs are studied in chapter4. A distinct feature of targets array is presented based on binary distance relationship of nodes for recognizing the side by side arrays. And then, the Hu invariant moments are modified for decribing the spatial distribution shape of discrete point sets. Some useful features for telling the lining, the circling, the grouping distribution structures of point sets are presented. And then, a comparation study of fuzzytopology is carried out. A fast fuzzytoplogy caculation method which is suitable for processing large remote sensing images based on the fuzzytoplogy envirnment morphology transfer alogorithm is presented. The presented method is more efficient that Bloch's method. At last, some formulae for caculating "Near" and "Surround" fuzzytoplogy more intuitively are presented.A novel targets' spatial distribution structure detection algorithm which we call as max spatial regularity detection algorithm (MSR detection algorithm)based on fuzzy information integration is proposed in chapter5. The aim of algorithm is to get a spanning tree constraint by the paths that have max spatial relationship regularity. By utilizing the minimum cast spanning tree growth mechanism of Prim algorithm, MSR integrates fuzzy spatial information by
estimating the fuzzy spatial relationship regularity among neighboring nodes by back searching the path in the found tree and forward detecting the nearest nodes, and detects the regular spatial relationship among targets initiatively as its can. The detected MSR spanning tree can be considered as the fuzzy evaluation of the regularity of array's spatial distribution structure, and its' some features can be used to detect targets' array. The experiments show that the algorithm iseffective and stable. The complexity of algorithm is maintaining O(N2), where N is thenumber of targets in the array.The problems and content needed further research are pointed out in chapter 6.
首先分析了数据源的特点，确定使用模糊理论与图论分析军事阵地目标群中的子目标特性与目标阵列群的空间分布结构。
第二章使用Kubelka-Munk光照物理模型分析了可见光彩色信息中一些不受光照环境、视点位置、物体材质的菲涅耳反射系数影响的不变量；之后使用高斯积分分析了光谱能量分布结构，提出了与设备参数相关的彩色不变量实际求解方法。所求彩色不变量可作为特征，对遥感彩色图像进行粗分割。并提出一种基于特征量化的快速类数自适应模糊C均值分级聚类方法，用于快速提取子目标斑点。
第三章使用ARG模型描述军事阵地目标群，使用图论中的路径描述节点之间的多元关系R~m，拓展传统ARG模型为AR~mG模型，用于分析多个目标之间的多元关系与空间分布结构。之后采用多尺度的概念，提出了多层化的AR~mG模型，mAR~mG，用以分析不同尺度对象之间的关系。该模型具有G(G，E)的嵌套形式，节点不再是单独的节点，而可能是一个子图，子图在mAR~mG中的某一尺度上被作为独立节点来处理。之后将mAR~mG模型模糊化为模糊mA(?)~mG模型，为模糊信息融合提供信息源。
第四章分析了子目标斑点的几何形状特征。结合二元距离与相似关系，提出了目标阵列等间距分布关系的显著特征：目标之间的最小距离相等。之后针对Hu的矩不变量的尺度不变性进行了参数修正，使之适合于分析离散斑点集的整体空间分布形状。并提出可区分环状、团状、线状分布斑点集的重心距离特征与圆形度特征。之后对目前已有的模糊拓扑空间关系求解方法进行了比较研究。针对模糊拓扑环境传递算法进行了较深入的研究，提出了适合对大图像进行模糊拓扑空间关系求解的改进方法，提高了算法效率。最后提出了比以往定义更加符合人的直觉习惯的“毗邻”与“包围”模糊拓扑空间关系的计算式。
第五章提出一种新颖的基于模糊信息融合的可用来检测目标之间空间分布结构规则程度的路径空间关系约束支撑树算法，即最规则空间分布关系MSR(Max Spatial Regularity)检测算法。算法基于“约束”支撑树的思想，借助Prim算法搜寻最小支撑树的机制，评估出当前节点与已搜索节点，以及未搜索的邻近节点之间满足某种有规则的空间分布关系的满意程度，约束支撑树的生长过程，尽最大可能地主动探测子目标之间可能存在的规则的空间分布结构；最后得到一棵可用来评估目标群满足某种局部的规则空间分布关系满意程度的MSR支撑树；对MSR支撑树进行解模糊，推测目标群是否为目标阵列。算法具有良好的抗噪声性能，且计算量维持在O(N~2)的量级，N为子目标数目。
最后系统地总结了本文的研究工作，给出了进一步研究的建议和设想。
Military targets arrays' features, especially the spatial distribution features in remote sensing images are studied in this paper.Firstly, the characteristics of resource data are discussed. And then, the fuzzy theory and the graph theory are confirmed to be the main research theories of this paper.Some color invariant features which invariant with the lighting condition, the view point position, the Fresnel reflectance coefficient of object's material are studied based on the Kubelka-Munk physical illumination model in chapter 2. The spectrum energy distribution structure is studied by using Gaussian color model, and a color invariance calculation method from RGB component of images is proposed. The color invariant features are used for fast segmentation of colored remote sensing, or multi-spectral images. A fast cluster number adaptive fuzzy C mean algorithm based on feature quantitative is proposed for extracting the sub-target blob efficiently.The military targets array is customized by ARG model in chapter3. By investigating the "path" concept of graph theory, the traditional ARG model is extended to ARmG model, and a multi-side relationship system Rmof the nodes based on the path description is established. As a result, ARmG model is used for analyzing the spatial distribution relationship among targets by studying the shape of the path that connects tagets. A multi-layered ARmG (mARmG) model is proposed for analyzing the multi-scale structure of targets' relationships. The model has the form as G(G,E}, in which the node can be considered as a sub-graph. And then, the relationshipattributes of mARmG model are defined by fuzzy set. The mARmG model is extended to mARmG model, and becomes the data resources for fuzzy information integration.The geometric attributes of target blobs are studied in chapter4. A distinct feature of targets array is presented based on binary distance relationship of nodes for recognizing the side by side arrays. And then, the Hu invariant moments are modified for decribing the spatial distribution shape of discrete point sets. Some useful features for telling the lining, the circling, the grouping distribution structures of point sets are presented. And then, a comparation study of fuzzytopology is carried out. A fast fuzzytoplogy caculation method which is suitable for processing large remote sensing images based on the fuzzytoplogy envirnment morphology transfer alogorithm is presented. The presented method is more efficient that Bloch's method. At last, some formulae for caculating "Near" and "Surround" fuzzytoplogy more intuitively are presented.A novel targets' spatial distribution structure detection algorithm which we call as max spatial regularity detection algorithm (MSR detection algorithm)based on fuzzy information integration is proposed in chapter5. The aim of algorithm is to get a spanning tree constraint by the paths that have max spatial relationship regularity. By utilizing the minimum cast spanning tree growth mechanism of Prim algorithm, MSR integrates fuzzy spatial information by
estimating the fuzzy spatial relationship regularity among neighboring nodes by back searching the path in the found tree and forward detecting the nearest nodes, and detects the regular spatial relationship among targets initiatively as its can. The detected MSR spanning tree can be considered as the fuzzy evaluation of the regularity of array's spatial distribution structure, and its' some features can be used to detect targets' array. The experiments show that the algorithm iseffective and stable. The complexity of algorithm is maintaining O(N2), where N is thenumber of targets in the array.The problems and content needed further research are pointed out in chapter 6.
引文
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