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用偏最小二乘法提取石头口门水库水色信息 总被引：1，自引：0，他引：1
水体的高光谱数据在提供大量信息的同时，其波段间存在很高的相关性，常规的统计方法反演水质参数不但不能充分利用这些信息，并且也不能很好的去相关，而偏最小二乘回归分析可以较好的解决这一问题。因此本研究通过利用高光谱仪在石头口门水库进行反射光谱测量和同步水质采样分析，建立了叶绿素a和悬浮物含量的偏最小二乘回归模型。结果表明：该模型能较好的利用高光谱数据信息，各光谱波段自变量在最终模型中的系数大小在一定程度上较符合叶绿素a和悬浮物的光谱吸收、散射特性；通过与常规的比值模型、一阶微分模型进行对比，偏最小二乘回归模型明显优于前两者，其各决定系数均高于0.7，因此估测效果较理想，可用于内陆二类水体的水色信息提取。 相似文献
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卫星遥感找矿应用已有30多年的历史了,但应用卫星遥感数据开展与矿化有关的信息提取的却仅有十几年历史,随着多光谱、高光谱卫星遥感数据的大量获取,这种方法也逐渐走向成熟.本文就卫星遥感信息提取找矿原理与应用实例进行说明. 相似文献
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基于面向对象的香榧资源分布遥感调查研究 总被引：2，自引：0，他引：2
香榧系第三纪孑遗植物,为我同特有的珍贵经济树种.常规的香榧资源调查方法存在工作量大、数据时效性差等不足.近年来高分辨率遥感影像的应用,为特定树种信息提取提供了可能.基于此,本文以高分辨率IKONOS卫星影像为基础,采用面向对象的信息提取方法,多尺度分割形成对象后,利用光谱、形状、纹理等构建特征空间,进行会稽山区香榧信息提取的试验,并与常规监督分类法(最大似然法)进行了比较.结果显示,基于面向对象方法的香榧信息提取精度达到86.57%,比监督分类法的精度提高了27.90%.研究表明,用面向对象的分类方法进行香榧信息提取和资源调查是可行的. 相似文献
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对遥感信息地学特征理论、高光谱分辨率遥感信息机理与地物识别、地物微波遥感信息处理与成像机理以及地表遥感信息在介质中传输规律 ,进行原理创新探索与实验技术研究。提出了地物粗糙表面几何参数与物理参数分离理论 ,阐述了相应的实验结果。在研制了先进面阵推扫式高光谱成像仪的基础上 ,研究分析了地物的高光谱成像规律、分类识别和信息提取。研究了多波段多极化成像雷达遥感信息机理、典型地物识别、特征信息提取和地物分类及识别方法。发展和阐述了遥感气溶胶谱光学厚度的宽带消光法 ,并用于北京等地多年气溶胶光学厚度资料的反演研究 相似文献
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氮素胁迫下的冬小麦光谱特征提取与分析 总被引：24，自引：2，他引：24
高光谱遥感是对地观测的重要技术手段，利用野外光谱仪在地面的实测工作为其在精准农业等方面的应用进行有益的尝试，可以提高作物营养诊断的精度。养分胁迫下高光谱特征提取是这一目标的地面预研究。本次试验对不同氮素养分胁迫下东小麦的不同生育期，分别观测其光谱反射率，分析其生物物理参数的变化规律和反射光谱的特征，利用导数光谱技术对叶绿素密度和叶面积指数等生物物理参量同原始光谱、一阶倒数光谱进行拟合度比较，结合养份胁迫的特点，分析建立光谱模型的可能性。叶绿素密度在可见光和近红外波段均拟合较好，拟合度在0.5左右，高于叶绿素含量与光谱反射率的拟合，而叶面积指数与冠层光谱拟合在近红外波段较好，在可见光波段拟合度较小。通过作物的光谱特征，提取其中重要的近红外反射峰值、绿峰和红端位移特征，与冬小麦的叶绿素密度、叶绿素含量等生物物理参数进行相关分析，建立了线性光谱模拟模型。提取出的特征参量均可有效地模拟生物物理参数的变化，其中叶绿素含量与近红外反射峰值的拟合度（R2)除乳熟期外，都在0.9以上；孕穗期的模拟模型也有显著的相关关系。综合分析，孕穗期是利用高光谱遥感进行作物长势和养份分诊断研究的最佳时期。 相似文献
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CBERS-02CCD图像中居民点用地信息提取方法研究 总被引：1，自引：0，他引：1
以前研究利用卫星图像对居民点用地信息提取的资料主要是Landsat TM、ETM 或者SPOT HRV数据,而利用中巴地球资源卫星CCD数据提取的研究较少。本研究利用中国资源卫星应用中心提供的CBERS-02CCD数据对居民点信息进行获取,对中巴卫星数据的推广应用具有一定的意义。由于不能直接利用中巴卫星数据进行归一化建筑指数(NDBI)计算以实现对居民点用地信息的提取,所以应探讨采用其他方法。首先,在对图像进行几何精校正以及投影和坐标系统转换预处理后,对图像中的主要地物光谱特征进行分析,再对图像进行归一化植被指数(NDVI)和归一化水体指数(NDWI)计算以扩大感兴趣地物与其背景的差异,确定合适的阈值从图像中提取植被、农田、水域、山体阴影、裸地和道路等背景信息,然后居民点用地信息通过去除以上背景信息的方法间接获取。最后,信息提取结果与2004年土地利用现状更新调查数据比较以检验提取结果的精度。经过精度检验表明,利用阈值分割法从图像中提取居民点信息的整体用户精度和制图精度分别达到81.4%和77.9%,该方法对获取1:5万及小于1:5万比例尺的居民点用地专题图制图精度较高,能够满足制图要求。 相似文献
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一种新的信息提取技术与内容管理 总被引：1，自引：0，他引：1
介绍了一种新的信息提取技术及其内容管理；自然语言处理和包装感应是实现这种新技术的两种主要方法。此外，还有XML和隐藏网页。利用信息提取技术，可以将网页变成一个数据库。信息提取技术可以方便地在网络上从不同来源的各种格式的文本中获取信息。提出了信息提取技术面临的巨大挑战。 相似文献
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【目的/意义】为了发现更全面、更具有普适性的反恐情报信息,本文在单层次关联规则挖掘的基础上研究
反恐情报的多层次关联规则挖掘方法。【方法/过程】根据反恐情报的数据特征提出统一最小支持度和多单项最小
支持度参数并用的方式筛选多层次涉恐特征频繁项集,在情报分析过程中保存部分特殊的冗余频繁项集、冗余多
层次关联规则和无趣多层次关联规则。【结果/结论】本文的研究可以发现涉恐数据中不同概念分层的关联规律。
[创新/局限] 文中提出的关联分析方法能够弥补普通的单层次关联规则挖掘在分析包含多层属性的涉恐数据中存
在的不足,为反恐预警和反恐决策提供更丰富、更科学、覆盖范围更广的参考。 相似文献
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Arabic is a widely spoken language but few mining tools have been developed to process Arabic text. This paper examines the crime domain in the Arabic language (unstructured text) using text mining techniques. The development and application of a Crime Profiling System (CPS) is presented. The system is able to extract meaningful information, in this case the type of crime, location and nationality, from Arabic language crime news reports. The system has two unique attributes; firstly, information extraction that depends on local grammar, and secondly, dictionaries that can be automatically generated. It is shown that the CPS improves the quality of the data through reduction where only meaningful information is retained. Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering of the crime reports, based on crime type. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data are cleansed by removing noise. The proposed system is validated through experiments using a corpus collated from different sources; it was not used during system development. Precision, recall and F-measure are used to evaluate the performance of the proposed information extraction approach. Also, comparisons are conducted with other systems. In order to evaluate the clustering performance, three parameters are used: data size, loading time and quantization error. 相似文献
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研究了基于CityGML的多层次细节3D城市模型快速建模方法,并将其结合到3DUGIS中用于数字城市建设。建模过程中考虑空间关系、拓扑关系和语义信息,建模过程自动化程度高,能够在三维空间直接进行空间分析,方便海量数据的交换与数据挖掘。并给出了上海世博园区三维信息系统结合具体应用案例。 相似文献
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文本挖掘是基于非相关文献知识发现的核心。本文将文本挖掘的过程细化为从文献源到初始文献集子过程,从初始文献集到中间词集子过程,从中间词集到关联词集子过程。并对每一个子过程中所使用的方法进行分析比较。在此基础上对文本挖掘存在的问题进行分析,并提出改进方法。 相似文献
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很多应用中需要对海量信息进行数据处理、动态分析,但目前还无法从大量数据中自动提取定性规则。因此,迫切需要一种能够从海量数据中自动提取有效信息、及动态分析的方法。数据挖掘技术可以实现上述功能,但难以对海量数据空间进行有效划分。本文将云模型应用到数据挖掘领域,克服了传统数据挖掘方法在数据空间划分上的不足,提出一种二维尺度云变换方法,有效地实现了定性规则提取。 相似文献
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Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes. 相似文献
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《Journal of The Franklin Institute》2023,360(9):6194-6210
Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. These data are often collected and stored across different institutions (banks, hospitals, etc.), or termed cross-silo. In this context, cross-silo federated learning has become prominent to tackle the privacy issues, where only model updates will be transmitted from institutions to servers without revealing institutions’ private information. In this paper, we propose a cross-silo federated XGBoost approach to solve the federated anomaly detection problem, which aims to identify abnormalities from extremely unbalanced datasets (e.g., credit card fraud detection) and can be considered a special classification problem. We design two privacy-preserving mechanisms that are tailored to the federated XGBoost: anonymity based data aggregation and local differential privacy. In the anonymity based data aggregation scenario, we cluster data into different groups and using a cluster-level data feature to train the model. In the local differential privacy scenario, we design a federated XGBoost framework by incorporate differential privacy in parameter transmission. Our experimental results over two datasets show the effectiveness of our proposed schemes compared with existing methods. 相似文献
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Yunhua Hu Hang Li Yunbo Cao Li Teng Dmitriy Meyerzon Qinghua Zheng 《Information processing & management》2006
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles. 相似文献
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Text mining techniques for patent analysis 总被引：1，自引：0，他引：1
Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus- and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a real-world patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches. 相似文献
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本文采用数据挖掘技术和情报语言学方法 ,构建了一个可以用于从因特网上提取信息、进行自动标引和自动分类的系统 ,提供了一种创建自动分类知识库的新方法 ;提出了一种用于主题抽取的位置加权算法 ,研制了一种改进汉语同义词识别性能的新方法 ,并在自动分类时运用了这种语义相似度识别算法。最后还对该系统性能进行了测试 相似文献