共查询到18条相似文献，搜索用时 140 毫秒
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徐艳 《测绘与空间地理信息》2019,42(1)
针对模糊K-均值算法依赖于群集原型的初始估计和对于数据中所存在的子群数目做出假设的缺点,结合最大似然估计,提出了不依赖先验假设的模糊聚类法——基于模糊最大似然估计的遥感影像分割算法。该算法在模糊最大似然估计算法中用模糊协方差来计算后验概率,用后验概率矩阵代替隶属度矩阵来进行划分。先用模糊K-均值进行图像预处理,然后用模糊最大似然估计算法进行分割。此外,本文用性能指标参数——超体积指标FHV来评价最优的类别数目。本文通过对模拟影像和真实影像的实验,验证了该算法的有效性和准确性。 相似文献
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基于欧式距离的K-均值聚类算法是一种硬分类(把每个待辨识的对象严格地划分到某个类中)方法,面对具有不确定性和混合像元特征的遥感图像数据,传统K-均值聚类算法很难得到满意的分类结果.为解决这一难题,将集对分析(set pair analysis,SPA)理论推广到遥感图像聚类算法,通过引入一个能统一描述同一性、差异性和对立性的同异反(identical discrepancy contrary,IDC)联系度,提出了基于IDC联系度的改进的K-均值聚类算法.该方法克服了传统K-均值算法硬分类的缺陷,可以有效地提高遥感图像聚类精度.对Landsat5 TM卫星数据的聚类分析实验表明,在含有混合像元的遥感图像地物覆盖分类中,改进的K-均值聚类方法的分类效果要优于传统K-均值聚类方法. 相似文献
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K-Means算法是比较流行的局域聚类算法,但由于其存在需要输入聚类数目以及对初始聚类中心敏感等缺陷,本文提出了一种基于密度的加权K-Means聚类算法来初始化聚类中心。该算法定义了点的密度函数和聚类中心函数,通过一定评价函数获取聚类中心。该方法获取的聚类中心不仅周围密度比较大,而且各个聚类中心之间相关性比较小,从而有效的减少了聚类时间,提高算法效率。 相似文献
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分带K-均值聚类的平面标靶定位 总被引：1，自引：0，他引：1
提出了一种分带K-均值聚类的平面标靶定位方法.根据标靶与测站距离的限制条件,推导了较大噪声点的剔除公式,在整体最小二乘拟合平面的基础上增加了噪声点二次剔除的方法,对经过噪声点剔除的点云数据进行分带、聚类处理.同时,对每一带的聚类中心进行均值化处理,得到每一带的中心点,通过求取不同带中心的均值来确定标靶中心点.实验结果表明,分带K-均值聚类的平面标靶定位模型较适合于平面标靶同名点确定. 相似文献
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针对传统上单独采用K-means或DBSCAN等方法对共享单车位置数据聚类时造成的聚类结果与真实的聚类结构不符的问题,本文提出了一种基于共享单车时空大数据的细粒度聚类方法(FGCM)。该方法通过DBSCAN进行初始聚类,并在此基础上采用GMM-EM算法进行细部聚类,以提取细粒度层级的热点区域。试验表明,该方法可根据密度阈值排除噪声和离群值,无需指定细部聚类簇数,簇的形状和大小比较灵活。在对共享单车大数据位置特征进行聚类时,与传统的单独采用K-means或DBSCAN的方法相比,FGCM具备更高的精细程度,能够充分展现共享单车的实际聚集特征,可用于规划共享单车电子围栏等设施,在不降低通勤效率的基础上规范共享单车的停放问题。 相似文献
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校车站点布局问题是一种典型的设施选址问题.大多数设施选址问题均属于区域选址,将设施选址限定在一定空间范围内,其方法并不适用于需要将位置固定在道路上的校车站点布局问题.因此,为保证生成的站点分布在路网上,且学生到站点的步行距离较短,提出了一种改进的K-means聚类算法,以一定范围内密度最大的点为初始类中心,在逐次迭代中... 相似文献
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National borders play an important role in everyday life. Interest in border studies has increased with recent changes in geographical locations of the border or the fluctuation of the permeability of the border between some countries, such as in the European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter border control, having appropriate information of the border is crucial for effective policymaking. The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputational methods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, were applied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, an object can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of the natural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-means clustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters are required to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neural network and was chosen for its ability to automatically determine the number of classes and handle categorical data. The Self-Organizing Map is unique because it can transform high dimensional data into low dimensional display while preserving the topology and spatial distribution of the input parameters. The results of the two classification methods suggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However, more research is needed to determine the fit of these algorithms for particular spatial data classification tasks. The results obtained from this research provide an insight into the permeability of the border region of Carinthia, Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism development and road transportation management in that region. Furthermore, the approach presented in this article can be applied to other national borders to identify zones permeable to pedestrian traffic. 相似文献
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超谱遥感图像快速聚类无损压缩算法 总被引：1，自引：0，他引：1
K-means聚类要求每个像素要和所有聚类中心求欧氏距离，当聚类数很多时，这是一个相当耗时的工作。改进的K—meam聚类算法根据历史聚类结果进行初始类分割，即节约初始聚类时间，又能使历史聚类过程中形成的类间稳定关系得以保持；类内像素只和相邻的聚类中心计算距离进行聚类，随着算法的迭代进行，大量类的状态基本固定，使得聚类速度不断加快。基于改进K-means聚类的无损压缩算法具有充分利用历史聚类成果和收敛速度快的特点，通过提高类内像素冗余度，最大限度消除谱间冗余和空间冗余。采用多次聚类压缩的结果预测最佳聚类数的方法，可实现最小熵无损压缩。通过和DPCM算法概率模型的熵值比较及实验数据的分析，验证了基于聚类无损压缩效率比不聚类无损压缩效果更优。 相似文献
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《International Journal of Digital Earth》2013,6(5):471-489
ABSTRACTSymmetry is a common feature in the real world. It may be used to improve a classification by using the point symmetry-based distance as a measure of clustering. However, it is time consuming to calculate the point symmetry-based distance. Although an efficient parallel point symmetry-based K-means algorithm (ParSym) has been propsed to overcome this limitation, ParSym may get stuck in sub-optimal solutions due to the K-means technique it used. In this study, we proposed a novel parallel point symmetry-based genetic clustering (ParSymG) algorithm for unsupervised classification. The genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym. A message passing interface (MPI) was used to implement the distributed master–slave paradigm. To make the algorithm more time-efficient, a three-phase speedup strategy was adopted for population initialization, image partition, and kd-tree structure-based nearest neighbor searching. The advantages of ParSymG over existing ParSym and parallel K-means (PKM) alogithms were demonstrated through case studies using three different types of remotely sensed images. Results in speedup and time gain proved the excellent scalability of the ParSymG algorithm. 相似文献
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