基于改进的模糊c-均值聚类方法遥感影像分类研究(英文)

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel （GK） and Gath-Geva （GG） algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.     

粗糙集理论进行遥感图像监督分类的样本质量评价的研究(英文)

In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.     

Management of spatio-temporal data of Cadastral Information System in China

Cadastral Information System (CIS) is designed for the office automation of cadastral management. With the development of the market economics in China, cadastral management is facing many new problems. The most crucial one is the temporal problem in cadastral management. That is, CIS must consider both spatial data and temporal data. This paper reviews the situation of the current CIS and provides a method to manage the spatiotemporal data of CIS, and takes the CIS for Guangdong Province as an example to explain how to realize it in practice.     

Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.     

An efficient representation of quadtrees and bintrees for multiresolution terrain models

A space-filling curve in 2,3,or higher dimensions can be thought as a path of a continuously moving point.As its main goal is to preserve spatial proximity,this type of curves has been widely used in the design and implementation of spatial data structures and nearest neighbor-finding techniques.This paper is essentially focused on the efficient representation of Digital Ele-vation Models(DEM) that entirely fit into the main memory.We propose a new hierarchical quadtree-like data structure to be built over domains of unrestricted size,and a representation of a quadtree and a binary triangles tree by means of the Hilbert and the Sierpinski space-filling curves,respectively,taking into account the hierarchical nature and the clustering properties of this kind of curves.Some triangulation schemes are described for the space-filling-curves-based approaches to efficiently visualize multiresolu-tion surfaces.     

Mining knowledge from result comparison between spatial clustering themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.     

THE ESTABLISHMENT OF A MICRO--COMPUTER BASED GEOGRAPHIC INFORMATION SYSTEM

In this paper,authors introduce the establishment of a Micro-computer Based Geographic Information System(MCGIS),which is one of the subsystems of The Loess Plateau Geographic Information System (LP-GIS) being carried out by the REIS Laboratory and several universities of China on various types and levels of computer.MCGIS is developed on the micro-computer Gteat Wall-286.Its purpose is to providean information system with powerful perforrmance,which could be afforded by thosecounty-level units or departments,so that the general guide-line of multi-function,multi-level and multi-user can come to practice. The system MCGIS is composed of the spatial database management systems and some software packages for geographic analysis and other further data processing.The spatial database management svstem is the kernel part of the MCGIS,and other function packages are babed on it.The whole system remains open, i.e.,ready to be extende in function.     

Fuzzy topological relations between fuzzy spatial objects

Fuzziness is an internal property of spatial objects.How to model fuzziness of a spatial object is a main task of next generation GIS.This paper proposes basic fuzzy spatial object types based on fuzzy topology.These object types are the natural extension of current nonfuzzy spatial object types.A fuzzy cell complex structure is defined for modeling fuzzy regions,lines and points.Furthermore,fuzzy topological relations between these fuzzy spatial objects are formalized based on the 9intersection approach.This model can be implemented for GIS applications due to its scientific theory basis.     

An Urban Open Space Extraction Method： Combining Spectral and Geometric Characteristics

This paper introduces an advanced method based on remote sensing and Geographic Information System for urban open space extraction combining spectral and geometric characteristics. From both semantic and remote sensing perspectives, a hybrid hierarchy structure and class organization of open space are issues and mapped from one to another. Based on per-pixel and segmentation mechanism separately, two classification approaches are performed. Owing to prior of spatial aggregation and spectral contribution, the segmentation-based classification exhibits its superiority over a pixel-based classification. Finally a GIS-based post procedure is hired to eliminate some unsuitable open space components in both spatial and numerical constraints on the one hand, and separate open space some fabrics from fused remote sensing classes by defining their Shape Index on the other hand. The case study of Beer Sheva based on ASTER data proves this method is a feasible way for open space extraction.     

空间数据模糊聚类的有效性评价

基于模糊划分中存在的分类不确定性因素和空间数据的空间位置特征,提出了一种新的空间数据模糊聚类有效性函数。实验结果表明,这种新的有效性函数能够对模糊聚类结果的有效性进行正确的评价,特别是对于空间数据模糊聚类有效性评价,其分类效果较理想,同其他有效性指标相比,能得到较优的分类数。     

基于空间加权距离的自适应Fuzzy C-Means算法研究

空间聚类不仅应考虑GIS对象属性特征的相似性,还应考虑对象的空间邻近性。不同属性、位置特征在聚类中起到的作用不同。采用信息熵方法计算空间距离中各属性距离、位置距离的权重,权值大小用于度量相应特征在fuzzy c-means隶属度计算时的作用大小,并引入相似性指标,当两个聚类之间的相似度高于某个合并阈值时,则对应的一对聚类进行合并,从而克服需预先设置聚类类数的问题。通过应用实例的聚类有效性分析,与普通空间距离相比,基于空间加权距离的FCM算法具有稳定性和有效性。     

一种新的融合空间信息的半监督变化监测方法

基于改进的半监督FCM算法和马尔科夫随机场,提出了一种新的融合空间信息的半监督变化监测方法。首先将两幅遥感图像相减得到差值图像,并通过第4波段的差值给出了一种新的样本标记方法;然后,通过标记样本对差值图像利用半监督FCM算法进行聚类;最后,为了提高监测精度和去除聚类噪音点,利用像元点之间的空间邻接关系和马尔科夫随机场,通过更新后的隶属度矩阵得到了监测结果。为了验证本文方法的有效性,选取了两组TM遥感图像,监测了森林的变化。试验结果表明,改进的半监督FCM算法可以减少监测的漏检率,马尔科夫随机场方法可以很好地去除聚类过程中形成的噪声点,减少监测的虚检率。     

基于MRF随机场的多光谱遥感影像最优化分级聚类

有限混合模型FM的分级聚类已广泛应用于不同领域,然而,由于它的计算复杂度与观测数据量平方成正比,致使在遥感影像方面应用受到了限制。另外,多光谱图像能提供空间和光谱两类信息详细的数据,但是,大多数多光谱图像聚类方法是基于像素的聚类,仅使用了其光谱信息而忽视了空间信息。本文定义一个相对混合密度函数,通过引入一个q-参数来调节各成分密度对其混合分布的贡献,提出一种广义有限混合模型GFM．设计一种新的适用于多光谱遥感影像的GFM分级聚类算法。该算法把MRF随机场和GFM模型结合在了一起,分类数通过PLIC准则自动确定。最后,利用仿真结果验证该算法的有效性,同时通过与K均值聚类、FM分级聚类以及SVMM分级聚类的比较说明本文算法的优越性。     

Mapping the porosity of international border to pedestrian traffic: a comparative data classification approach to a study of the border region in Austria,Italy, and Slovenia

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.     

基于Visual C#.NET的模糊聚类分析系统及其应用

由于事物的区分通常具有模糊性,采用模糊聚类方法进行分类更符合实际。介绍了模糊聚类分析的基本思想,用传递闭包法进行聚类分析,基于Visual C#.NET语言研制了一个模糊聚类分析系统,并应用于形变监测网的分析。结果显示,其分类结果符合实际要求。     

克服双重约束的面目标位置聚类方法

面目标的聚集模式识别是空间聚类研究的重要方向之一,但因多边形几何信息和空间障碍阻隔的双重约束,目标的位置相似性难以快速而准确地计算。扩展点目标多尺度聚类方法,通过构建面目标的强度函数计算目标与邻近目标的位置聚集程度,提出了有效作用于双重约束下的面目标位置聚类法,并以判断相邻尺度下同一面目标类的强度函数阈值相等作为算法的收敛条件。经试验分析与比较发现,算法无须自定义参数,能够识别密度不均、任意形状分布,以及"桥"链接的面目标集群,同时能够准确判断障碍约束对面目标簇的阻隔和划分。     

AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA

l IntroductionClassification pIays an imPOrtant role for rernotelysensed data tO be intngrated into gapraphical infOr-mation systems(GISs), and is increasingly comPut-eriZed with soPhisticated hardware and software(Cambell l987; Lillesand and Kiefer l994). Pnd-ucts Of classification are usua[ly represented in formof contiguous patches of pixels,with each being la-belled as belonging to a discrete and dominantclass. Such tyPe of classification is termed as crispor discrete. The accuracie…     

基于自适应膨胀因子的模糊覆盖分级方法

分级界限的确定一直都是学者们关注的焦点,而如何确定分级数则往往被忽略了。根据模糊覆盖和膨胀因子的定义,提出了一种确定1维数据分级数的分级方法:首先通过覆盖半径做一个模糊覆盖,利用自膨胀因子对这个覆盖进行扩张,直到不能再向外扩张为止,这一级就确定了;然后进行下一级的确定。该方法不仅可以自动确定数据的分级数,而且克服了分级结果对参数的敏感性。最后用实验证明了方法的有效性。     

一种顾及上下文的遥感影像模糊聚类

模糊聚类是非监督分类中的一类重要方法。传统的模糊聚类方法应用于遥感影像的非监督分类时，均未考虑到邻域像元间的统计依赖关系即上下文信息。针对这一缺陷，在Markov随机场模型框架下，引入了空间隶属度概念，提出了一种顾及上下文信息的模糊聚类算法，有效地提高了聚类精度和抗噪声能力。针对需要预先指定聚类个数的问题，采用了一种兼顾类别内部紧密程度和类别之间分离程度的评价指标，用以检验聚类结果的有效性。从而找出最优的聚类个数，在一定程度上提高了聚类结果的客观性。最后通过实验验证了本文算法的有效性。