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地物波谱数据库研究现状与发展趋势 总被引:24,自引:2,他引:24
随着定量遥感技术发展与遥感应用的逐步深入,地物波谱数据库愈加显示其在遥感领域中的重要技术支撑作用。本文着重阐述和分析了国内外地物波谱数据库研究现状和发展趋势,并结合我国当前遥感技术的发展和定量化应用需求,论述了我国未来地物波谱库发展必要性、可行性和总体构想。 相似文献
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面向分类数据的自组织神经网络 总被引:1,自引:2,他引:1
作为一种优良的聚类和降维工具,自组织神经网络SOM(SelfOrganizingFeatureMaps)已经得到广泛应用。其不足之处是仅适合于数值数据,这对时常需要处理分类型数据(Categoricalvalueddata)或数值型与分类型混合数据(Mixednumericandcategoricalvalueddata)的数据挖掘应用是不够的。该文提出了一种新的基于覆盖(Overlap)的距离函数并将其用于SOM训练。实验结果表明,在不增加时空开销的前提下可取得较好的聚类效果。 相似文献
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基于混合核函数的自组织神经网络遥感图像分类 总被引:1,自引:0,他引:1
自组织神经网络SOM作为一种无监督学习的竞争式网络,已经得到了广泛的应用,它通过对输入信号的竞争学习,将样本划为不同的类别,但其分类效果常很难令人满意.提出了一种基于混合核函数的SOM神经网络改进方法,并和传统的SOM网络进行了对比,Iris数据和Wine数据的分类实验表明,该方法可以明显改进SOM网络的分类效果.然后对某地Landsat卫星遥感图像数据进行分类实验,实验结果表明,与传统的SOM网络、基于多项式核的SOM网络以及基于RBF核的SOM网络相比较,基于混合核函数的SOM神经网络方法的分类效果有较明显的提高. 相似文献
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自组织特征映射神经网络——用于茶叶分类 总被引:1,自引:0,他引:1
介绍了自组织特征映射神经网络的结构和算法 ,将其用于中国茶叶的分类 ,并和传统方法进行了比较。认为组织特征映射神经网络优于传统方法。 相似文献
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为贴合地物表现的多样性和复杂性,提出一种波谱匹配支持下的遥感影像专题地物自适应提取方法,经过专题地物端元选取、波谱匹配、影像自动分割、\"整体-局部\"的空间转换,以及局部针对性、精细化地迭代逼近等一系列相互衔接的算法,全面、准确地提取遥感影像上的专题地物。通过在ETM+影像上水体和裸地的提取实验,并与最大似然法和SVM分类结果比较,证明了该方法对多样性专题地物提取的有效性和普适性。 相似文献
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中国典型地物波谱数据库的研究与设计 总被引:4,自引:0,他引:4
中国典型地物波谱库系统是一个集地物波谱数据库、地表先验知识库、遥感应用模型库与航空航天影像库为一体的综合遥感信息应用平台。较之已有的波谱数据库系统,该系统的创新点在于实现了多源知识的整合,不仅能为用户提供观测波谱,而且能够在地表先验知识库和模型库的支持下实现波谱库的时间尺度和空间尺度上的扩展。本研究了在系统建设过程中的三个关键问题:地面观测和配套参数管理,先验知识库以及模型驱动理论分析。基于B/S架构以及组件对象模型技术,给出了系统方案设计和实现技术。 相似文献
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本文依据现有地物波谱数据采集标准和自定义标准,对矿区各类地物的波谱数据库结构进行了设计,采用基GDI+技术的波谱曲线可视化技术、基于关系数据库的波谱分类管理技术以及渡谱数据质量控制技术等,已经实现了针对于矿区植被大类的农作物属性信息和光谱数据的批量入库、查询、显示等,并通过自主开发的图形控件实现了光谱曲线的批量绘制和比较,为进一步的地物波谱数据应用提供了联动的、丰富的原始观测信息。 相似文献
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地物波谱特性是遥感探测的基础,是定量遥感技术与应用发展的先决条件,加强地物波谱研究的同时应推动地物波谱数据库的建设。论文从典型地物波谱数据库系统总体结构组织、模块划分、功能分配、系统界面设计以及系统数据库设计等方面论述了典型地物波谱数据库系统设计的研究过程。 相似文献
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Stergios Papadimitriou Seferina Mavroudi Liviu Vladutu G. Pavlides Anastasios Bezerianos 《Applied Intelligence》2002,16(3):185-203
Complex application domains involve difficult pattern classification problems. The state space of these problems consists of regions that lie near class separation boundaries and require the construction of complex discriminants while for the rest regions the classification task is significantly simpler. The motivation for developing the Supervised Network Self-Organizing Map (SNet-SOM) model is to exploit this fact for designing computationally effective solutions. Specifically, the SNet-SOM utilizes unsupervised learning for classifying at the simple regions and supervised learning for the difficult ones in a two stage learning process. The unsupervised learning approach is based on the Self-Organizing Map (SOM) of Kohonen. The basic SOM is modified with a dynamic node insertion/deletion process controlled with an entropy based criterion that allows an adaptive extension of the SOM. This extension proceeds until the total number of training patterns that are mapped to neurons with high entropy (and therefore with ambiguous classification) reduces to a size manageable numerically with a capable supervised model. The second learning phase (the supervised training) has the objective of constructing better decision boundaries at the ambiguous regions. At this phase, a special supervised network is trained for the computationally reduced task of performing the classification at the ambiguous regions only. The performance of the SNet-SOM has been evaluated on both synthetic data and on an ischemia detection application with data extracted from the European ST-T database. In all cases, the utilization of SNet-SOM with supervised learning based on both Radial Basis Functions and Support Vector Machines has improved the results significantly related to those obtained with the unsupervised SOM and has enhanced the scalability of the supervised learning schemes. The highly disciplined design of the generalization performance of the Support Vector Machine allows to design the proper model for the particular training set. 相似文献
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基于小波神经网络的光谱数据表征与分类研究 总被引:3,自引:0,他引:3
介绍了一种新的神经网络模型——小波神经网络,利用它并适当选取网络结构和小波基,实现了对化学物质红外光谱的压缩表征和分类,取得了令人满意的效果,说明小波神经网络在光谱处理方面有着光明的应用前景和优越性。 相似文献
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The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM. 相似文献
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de Mesquita Roberto N. Ting Daniel K. S. Cabral Eduardo L. L. Upadhyaya Belle R. 《Real-Time Systems》2004,27(1):49-70
A new classification method, for isolating steam generator tube defects in nuclear power plants using Eddy Current Test (ECT) signals, has been developed. The method uses Self-Organizing maps (SOM) with different data signatures to identify and classify these defects. A multiple inference system is proposed which evaluates different extracted characteristic SOMs to infer the defect type. Wavelet zero-crossing representation, a linear predictive coding (LPC), and other basic signal representations, such as magnitude and phase, are used to construct characteristic vectors that combine one or more of these features. These vectors are evaluated for their ability to classify tube defects and the ones with the best performance are used in the multiple inference system. The effectiveness of the method is demonstrated by applications of the characteristic maps to ECT data from various cases of tube defects in pressurized water reactor plant steam generators. The developed algorithm enables real-time applications such as fast tube defects classification systems and visualization of ECT signal feature prototypes, which may improve the speed of time-critical decision making during power plant maintenance outages. 相似文献
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Self-Organizing Map (SOM) networks have been successfully applied as a clustering method to numeric datasets. However, it
is not feasible to directly apply SOM for clustering transactional data. This paper proposes the Transactions Clustering using
SOM (TCSOM) algorithm for clustering binary transactional data. In the TCSOM algorithm, a normalized Dot Product norm based
dissimilarity measure is utilized for measuring the distance between input vector and output neuron. And a modified weight
adaptation function is employed for adjusting weights of the winner and its neighbors. More importantly, TCSOM is a one-pass algorithm, which is extremely suitable for data mining applications. Experimental results on real datasets show that TCSOM
algorithm is superior to those state-of-the-art transactional data clustering algorithms with respect to clustering accuracy. 相似文献
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We describe a scalable parallel implementation of the self organizing map (SOM) suitable for data-mining applications involving clustering or segmentation against large data sets such as those encountered in the analysis of customer spending patterns. The parallel algorithm is based on the batch SOM formulation in which the neural weights are updated at the end of each pass over the training data. The underlying serial algorithm is enhanced to take advantage of the sparseness often encountered in these data sets. Analysis of a realistic test problem shows that the batch SOM algorithm captures key features observed using the conventional on-line algorithm, with comparable convergence rates.Performance measurements on an SP2 parallel computer are given for two retail data sets and a publicly available set of census data.These results demonstrate essentially linear speedup for the parallel batch SOM algorithm, using both a memory-contained sparse formulation as well as a separate implementation in which the mining data is accessed directly from a parallel file system. We also present visualizations of the census data to illustrate the value of the clustering information obtained via the parallel SOM method. 相似文献
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人工神经网络中的自组织特征映射网络具有较强的聚类功能,将自组织特征映射神经网络模型应用于土壤分类,提取影响土壤分类的七个理化因子,根据19个土壤样本建立神经网络,最后验证10个土壤样本的分类结果是否正确。分析结果表明,这种方法是十分有效和方便的。同时,本文对分类结果进行分析和讨论,指出利用该模型强大的学习功能及很好的自适应性、自组织性和鲁棒性可以为土壤分类提供一种快速、准确的信息处理手段。 相似文献
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一种新的基于遗传算法的数据分类方法 总被引:5,自引:0,他引:5
当前分类算法还存在诸如伸缩性不强、可调性差、缺乏全局优化能力等问题.通过构造完全分类规则集,设计了一种有效的遗传编码方法,使得遗传算法的各种优良特性在数据分类中得到充分的运用,从而提出了一种新的数据分类算法,新算法有效提高了数据分类的准确性,较好克服了当前存在的缺点.最后,给出了实验结果,证实了算法的有效性. 相似文献
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Feature Competitive Algorithm for Dimension Reduction of the Self-Organizing Map Input Space 总被引:1,自引:1,他引:0
The self-organizing map (SOM) can classify documents by learning about their interrelationships from its input data. The dimensionality of the SOM input data space based on a document collection is generally high. As the computational complexity of the SOM increases in proportion to the dimension of its input space, high dimensionality not only lowers the efficiency of the initial learning process but also lowers the efficiencies of the subsequent retrieval and the relearning process whenever the input data is updated. A new method called feature competitive algorithm (FCA) is proposed to overcome this problem. The FCA can capture the most significant features that characterize the underlying interrelationships of the entities in the input space to form a dimensionally reduced input space without excessively losing of essential information about the interrelationships. The proposed method was applied to a document collection, consisting of 97 UNIX command manual pages, to test its feasibility and effectiveness. The test results are encouraging. Further discussions on several crucial issues about the FCA are also presented. 相似文献