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基于中分辨率TM数据的湿地水生植被提取   总被引:5,自引:0,他引:5       下载免费PDF全文
林川  宫兆宁  赵文吉 《生态学报》2010,30(23):6460-6469
利用湿地水生植被生长旺盛、光谱反射较强、光谱信息比较丰富的8月份中分辨率Landsat TM和ETM+多光谱遥感影像,采用面向对象的分类方法,进行野鸭湖湿地水生植被的提取。研究表明:在提取过程中,通过对原始影像进行主成分变换和穗帽变换,将主要信息与噪声分离,不仅减小了数据冗余和波段间的相关性,而且增大了影像上湿地水生植被与其他地物类型光谱和空间信息的差异性,并结合野外水生植被光谱特征分析,选择归一化植被指数NDVI与归一化水体指数NDWI辅助分类,构建特征波段或波段组合,然后,确定适当的隶属度函数和阈值范围,构建分类决策树,完成湿地水生植被的自动分类,提高了影像分割与面向对象分类的精度,取得了较为理想的湿地水生植被提取结果。2002年和2008年两景影像的总体分类精度分别达到86.5%和85.44%,表明中分辨率TM影像可以满足湿地水生植被提取的需要,又因为其具有较高的波谱分辨率、极为丰富的信息量、相对较低的价格、长时间序列,可以作为近20a湿地水生植被提取和动态变化监测的主要数据源。  相似文献
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城市建筑物空间信息的获取对城市规划、环境保护等社会各行业越来越重要,高分辨率商业卫星的出现为提取建筑物3D信息提供了可能性.本文基于Barista软件,利用QuickBird数据提取了建筑物的3D信息并进行了精度验证.结果表明:基于Barista软件从高分辨率卫星影像中提取建筑物3D信息,具有专业水平要求低、普适性强、操作简单、精度高等优点;当数字高程模型(DEM)和传感器定位模型精度较高、影像偏天底角较理想时,3D信息提取的水平定位精度和高度测量精度可达到1个像素水平.  相似文献
3.
兰州中心城区用地扩展及其热岛响应的遥感分析   总被引:4,自引:0,他引:4  
以兰州中心城区为研究区,以1978、1993、2001和2010年的Landsat MSS/TM/ETM+影像为数据源,采用NDBI-SAVI指数相结合的方法,提取建设用地信息,利用热红外波段的单窗算法反演城市地表温度.结合城市热岛比例指数和地表温度分级,定量研究城镇用地扩张对城市热环境的影响.结果表明:兰州中心城区建设用地在1978-2010年的30余年间面积由87.47 km2增加到172.61 km2,增幅近l倍.城市扩展与热岛扩展趋势具有空间一致性,在城市扩张的同时,城区地表温度逐渐上升,城市热岛面积不断扩大.  相似文献
4.
Information extraction in molecular biology   总被引:3,自引:0,他引:3  
5.
Frontiers of biomedical text mining: current progress   总被引:3,自引:0,他引:3  
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.  相似文献
6.
Text mining and ontologies in biomedicine: making sense of raw text   总被引:1,自引:0,他引:1  
The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.  相似文献
7.
Leitner F  Valencia A 《FEBS letters》2008,582(8):1178-1181
We propose that the combination of human expertise and automatic text-mining systems can be used to create a first generation of electronically annotated information (EAI) that can be added to journal abstracts and that is directly related to the information in the corresponding text. The first experiments have concentrated on the annotation of gene/protein names and those of organisms, as these are the best resolved problems. A second generation of systems could then attempt to address the problems of annotating protein interactions and protein/gene functions, a more difficult task for text-mining systems. EAI will permit easier categorization of this information, it will help in the evaluation of papers for their curation in databases, and it will be invaluable for maintaining the links between the information in databases and the facts described in text. Additionally, it will contribute to the efforts towards completing database information and creating collections of annotated text that can be used to train new generations of text-mining systems. The recent introduction of the first meta-server for the annotation of biological text, with the possibility of collecting annotations from available text-mining systems, adds credibility to the technical feasibility of this proposal.  相似文献
8.
Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments. Electronic Publication  相似文献
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