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   crop identification 在 农业基础科学 分类中 的翻译结果: 查询用时:0.061秒
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crop identification    
相关语句
  作物识别
    Intensity-hue-saturation model based image fusion of SPOT-5 HRG1 data for crop identification
    用基于IHS变换的SPOT-5遥感图像融合进行作物识别
短句来源
    Crop identification based on MODIS time series data
    基于MODIS时序数据分析的作物识别方法
    Crop identification is the basic work in crop yield estimation.
    作物识别是遥感作物估产的基础。
  作物识别
    Intensity-hue-saturation model based image fusion of SPOT-5 HRG1 data for crop identification
    用基于IHS变换的SPOT-5遥感图像融合进行作物识别
短句来源
    Crop identification based on MODIS time series data
    基于MODIS时序数据分析的作物识别方法
    Crop identification is the basic work in crop yield estimation.
    作物识别是遥感作物估产的基础。
  农作物识别
    The model proved to be able to increase the accuracy of crop identification and image classification.
    通过分类验证,使用该模型进行图像增强后,有助于提高农作物识别与图像分类的精度。
    Image Spectral Difference Enhancement and Crop Identification
    遥感图像光谱差异增强与农作物识别
  农作物识别
    The model proved to be able to increase the accuracy of crop identification and image classification.
    通过分类验证,使用该模型进行图像增强后,有助于提高农作物识别与图像分类的精度。
    Image Spectral Difference Enhancement and Crop Identification
    遥感图像光谱差异增强与农作物识别

 

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The concept of data fusion was proposed in 1970s and developed in 1990s. Many researches have been done in this area. However, for a long time there is not a specific definition of data fusion that is commonly accepted and used by academic societies of information technology. The description of data fusion is usually different from one to another, which confuses the communication in the scientific community and influences the technology transfer to industry. On the other hand, how to apply the data fusion technology...

The concept of data fusion was proposed in 1970s and developed in 1990s. Many researches have been done in this area. However, for a long time there is not a specific definition of data fusion that is commonly accepted and used by academic societies of information technology. The description of data fusion is usually different from one to another, which confuses the communication in the scientific community and influences the technology transfer to industry. On the other hand, how to apply the data fusion technology achievements to remote sensing in agricultural condition monitoring is also a very important area. In this paper, the concept of data fusion and its development are reviewed in detail, including the definition, fusion levels, and methods. Then the paper analyses the status of its application in agricultural condition monitoring: high spatial and spectral resolution images begin to be used in agricultural condition monitoring together with low resolution ones, and some ancillary data play a more important role in helping with crop identification and image classification. For how to better use data fusion in agricultural condition monitoring, which is not only operational, on large scale, with great complexity, but also requires high accuracy, the application prospects are discussed by the authors.

长期以来,由于对数据融合一直没有一个严格的统一的定义,对于数据融合的理解、表达存在各种差异,在一定程度上影响了学术交流的顺利开展和应用渠道的畅通。另一方面,数据融合在遥感领域的长期发展已取得了丰硕的成果,将这些成果应用于农情遥感监测中,是非常有意义的。该文详细介绍了数据融合的概念及其发展过程,包括数据融合的定义、数据融合层次和融合方法;分析了当前数据融合在遥感尤其农情遥感监测中的应用现状,并针对农情遥感监测的特点,展望数据融合在农情遥感监测中的应用前景。

The theoretical and practical meaning of remote sensing monitoring of crop area is introduced. The (present) situation and existing problems of vegetation mapping and crop identification research at home and abroad based on routine method, linear frame sampling, area frame sampling, geographical knowledge base, spectral mixture model are reviewed. Suggestions are put forward for spectral library-based crop area research and (application) expanding.

介绍了遥感监测粮食作物播种面积的理论和现实意义,回顾了国内外基于常规方法、线状框架采样、面积框架采样、光谱混合模型(spectralmixturemodel,SMA)等方法进行植被制图和进行粮食作物识别研究的现状和存在的问题,并对基于波谱库的农作物播种面积监测提出了建议。

For meeting the demand for large-scale agricultural monitoring system with remote sensing technology,extracting crop information on the remote sensing image must be rapidly,precisely and reliably conducted.In this paper,the fall crop identification with Terra/MODIS was taken as an example in Beijing of China.Applying spectral analysis and time series characteristics,the decision tree algorithm was put forward,which can extract the main fall crops effectively and easily.Firstly,according to the spectral...

For meeting the demand for large-scale agricultural monitoring system with remote sensing technology,extracting crop information on the remote sensing image must be rapidly,precisely and reliably conducted.In this paper,the fall crop identification with Terra/MODIS was taken as an example in Beijing of China.Applying spectral analysis and time series characteristics,the decision tree algorithm was put forward,which can extract the main fall crops effectively and easily.Firstly,according to the spectral and biological characteristics of the fall crops,the spectral reflectances of MODIS were analyzed.One of red,blue,NIR and ESWIR band was selected as working band.Secondly,land surface water index(LSWI),which is defined by NIR and ESWIR,enhanced vegetation index(EVI),which is defined by Red,NIR and Blue bands were used as characteristic parameters to improve the precision.Finally,the decision tree algorithm was used for the fall crop identification.To verify the result,the extracting results were compared with the statistical result of State Statistics Bureau.The precision reaches 86%.This shows that it can obviously improve the crop identification accuracy with the decision tree algorithm and can be good enough to meet the operational method for agricultural condition monitoring with remote sensing and information service system at national-level.

为快速、准确地在遥感图像上提取各种农作物类型信息,满足国家农情遥感监测系统的要求,以2002年北京地区主要秋季作物提取为例,利用T erra/M OD IS数据,采用波谱分析的方法,建立一种基于遥感影像全覆盖的秋季作物类型自动提取方法,实现主要秋季作物遥感自动识别。首先根据研究区秋季作物的波谱特性和生物学特性,选取了红波段、蓝波段、近红外波段和中短波红外波段作为秋季作物类型提取的工作波段;同时,还利用由这4个波段构建的陆表水分指数和增强型指标指数作为遥感特征参量。其次根据研究区农作物物候历特征,提取了2002年4月到9月共7个时相的M OD IS数据。最后,采用分层决策树方法提取研究区主要秋季作物类型,并进行面积统计。为了验证其精度,与国家农业部农业统计数据进行比较,结果其精度达到86%以上。这表明,仅利用M OD IS自身光谱信息,即可较为准确地提取秋季作物类型信息,精度基本能满足了大尺度农情遥感监测的要求,可以为农业决策部门提供信息服务。

 
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