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optimal band combination     
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  波段组合
     In this paper,the optimal combination wavebands(B20,B1 and B29) are screened out from 36 MODIS wavebands for deriving the color-enhanced composed images of monitoring dust storms using EOS/MODIS data,optimal band combination index method and variance-covariance matrix eigenvalue method after analyzing and summarizing the predecessors' research achievements.
     利用EOS/MODIS数据,采用最佳波段组合指数法和方差-协方差矩阵特征值法,从36个MODIS波段中筛选出用于沙尘暴图像最佳彩色合成增强的组合波段是B20,B1,B29(BXX表示波段号位XX的反照率或亮温值,下同)。
短句来源
     The Information Characteristics of Thematic Mapper Data and the Optimal Band Combination
     TM图像的光谱信息特征与最佳波段组合
短句来源
     It is very important making optimal band combination in numerous bands for false color synthesis according to specific application purpose for further managements analysis and information extraction of hyperspectral remote sensing dada.
     如何根据具体的应用目的,在众多的波段中选取最佳波段组合用于假彩色合成以突出感兴趣的区域,对于有效的进行高光谱数据处理、分析及信息提取至关重要。
短句来源
     the optimal band combination is 431 which is applied to establish identification and analyse criterion of substances on earth surface for visual interpretation;
     最优波段组合为431,并在此基础上建立了地物判读分析依据,用于QuickBird遥感影像目视解译;
短句来源
  最优波段组合
     the optimal band combination is 431 which is applied to establish identification and analyse criterion of substances on earth surface for visual interpretation;
     最优波段组合为431,并在此基础上建立了地物判读分析依据,用于QuickBird遥感影像目视解译;
短句来源
  最佳波段组合
     In this paper,the optimal combination wavebands(B20,B1 and B29) are screened out from 36 MODIS wavebands for deriving the color-enhanced composed images of monitoring dust storms using EOS/MODIS data,optimal band combination index method and variance-covariance matrix eigenvalue method after analyzing and summarizing the predecessors' research achievements.
     利用EOS/MODIS数据,采用最佳波段组合指数法和方差-协方差矩阵特征值法,从36个MODIS波段中筛选出用于沙尘暴图像最佳彩色合成增强的组合波段是B20,B1,B29(BXX表示波段号位XX的反照率或亮温值,下同)。
短句来源
     It is very important making optimal band combination in numerous bands for false color synthesis according to specific application purpose for further managements analysis and information extraction of hyperspectral remote sensing dada.
     如何根据具体的应用目的,在众多的波段中选取最佳波段组合用于假彩色合成以突出感兴趣的区域,对于有效的进行高光谱数据处理、分析及信息提取至关重要。
短句来源
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  optimal band combination
To retrieve land use/cover information, optimal band combination scheme need to be selected.
      


As a case study of merging QuickBird multispectral and panchromatic remote sensing image, Brovey,IHS,PCA and Synthetic Variable Ratio(SVR) are choosen and their merging effects are evaluated quantificationally by difference index and average gradient; bands of merged image choosen to produce basic map are determined by optimium index factor(OIF). It is indicated that merging effect of SVR is beyond orther 3 algorithms;the optimal band combination is 431 which is applied to establish identification and...

As a case study of merging QuickBird multispectral and panchromatic remote sensing image, Brovey,IHS,PCA and Synthetic Variable Ratio(SVR) are choosen and their merging effects are evaluated quantificationally by difference index and average gradient; bands of merged image choosen to produce basic map are determined by optimium index factor(OIF). It is indicated that merging effect of SVR is beyond orther 3 algorithms;the optimal band combination is 431 which is applied to establish identification and analyse criterion of substances on earth surface for visual interpretation;tolerance-grid-digitalization method is used for screen-digitization in producing basic map for agricultural zone.

应用比值变换、IHS变换、主成分变换和合成比值变量变换等4种影像融合方法对QuickBird多光谱和全色遥感影像进行融合,并利用偏差指数、平均梯度等指标对融合效果进行定量评价;运用最佳指数因子确定参与成图的多光谱影像的波段.研究结果表明,合成比值变量变换法的融合效果最佳;最优波段组合为431,并在此基础上建立了地物判读分析依据,用于QuickBird遥感影像目视解译;采用容差格网矢量化技术进行遥感影像的屏幕矢量化,制作了1:2000比例尺农业园区底图.

In this paper, the significance measure Δ_η is used as the criterion for the classification effect of two kinds of ground objects, and the vegetation index-based method for water and land recognition using hyperspectral images is proposed. The water and land recognition experiment is made by taking the tidal flat area as an example. It is shown from the experimental results that the optimal vegetion index type and its optimal band combination can be selected based on the significance measure Δ_η, and this...

In this paper, the significance measure Δ_η is used as the criterion for the classification effect of two kinds of ground objects, and the vegetation index-based method for water and land recognition using hyperspectral images is proposed. The water and land recognition experiment is made by taking the tidal flat area as an example. It is shown from the experimental results that the optimal vegetion index type and its optimal band combination can be selected based on the significance measure Δ_η, and this method is applicable to the water and land recognition using hyperspectral image data.

提出了一种判别两类地物分类效果的依据———显著性度量,并采用植被指数的形式,给出了应用高光谱图像进行水陆识别的方法。以滩涂为例进行水陆识别实验,结果表明,基于显著性度量遴选得到的最佳植被指数类型及其最佳波段组合,适合于解决以高光谱图像为数据源的水陆识别问题。

The formation and role of Hyperspectral Remote Sensing Data Mining (HRSDM) are analyzed oriented to the characteristics and demands of hyperspectral information processing. The framework and processing flow of HRSDM are proposed at first. The knowledge that can be discovered from hyperspectral RS information includes spectral signatures, spatial rules, knowledge about genesis and diagnosis, spectral knowledge among different bands, dynamic evolution knowledge and isolated point identification. Five useful DM...

The formation and role of Hyperspectral Remote Sensing Data Mining (HRSDM) are analyzed oriented to the characteristics and demands of hyperspectral information processing. The framework and processing flow of HRSDM are proposed at first. The knowledge that can be discovered from hyperspectral RS information includes spectral signatures, spatial rules, knowledge about genesis and diagnosis, spectral knowledge among different bands, dynamic evolution knowledge and isolated point identification. Five useful DM modes are standard spectral database mining, spectrum-based mining, spatial dimension-based mining, spatial and spectral dimension mining and mining operations with support of auxiliary and background data. By analysis and experiments, some algorithms proved effective to HRSDM include association rule mining, clustering and artificial neural network (ANN), rough set and fuzzy theory, decision tree and data cube. Finally, some potential applications of HRSDM including typical information extraction, quantitative RS, RS inversion, image classification, mixed pixel decomposition, feature extraction and selection and optimal band combination are put forward.

面向高光谱遥感信息的特点,分析了高光谱遥感数据挖掘的形成和作用,在构建其框架体系与处理流程的基础上,探讨了可以发现的知识类型和典型的挖掘模式,并分析了一些主要挖掘算法和关键技术,最后对高光谱遥感数据挖掘潜在的应用方向进行了探讨。

 
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