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像元

网络pixel; Image element; pixels

像元

网络释义

像元

pixel

ENVI用户指南 - 小朱的日志 - 网易博客 ... 增长（ Grow）像元（ Pixel）感兴趣区类型（ ROI Types） ...

Image element

建筑专业| - 天圆地方建筑论坛 - Powered by... ... Image, 影像 Image element, 像素；像元；像点 Image geometry, 影像几何 ...

pixels

密暗植被法即利用影像中已知反射率的密暗像元(pixels)求取大气气溶胶的光学厚度，但若影像中的密暗像元极少或不存在的情 …

pixel size

　　● 像元（pixel size）——瞬时视域所对应的地面面积像元(pixe1)，即与一个像元大小相当的地面尺寸，单位为米(m)。它是遥感 …

Endmember

...光谱分析，混合像元是再分的，能被再分出的单元称终端像元（Endmember），或者译为“组分”或者“端元” 。

pixel-based

4).依判释单元 a.单像元(pixel-based)分类。 b.区域(地物)式分类(region-based/object-based)分类。

picture element

飞达光学网光电词汇港台地区译法(P) ... picture bit rate 图像位速率 picture element 像素，像元 picture field 图像场 ...

例句：

Subpixel imaging technology is a state-of-the-art method to reduce the size of camera and improve the spatial resolution .

亚像元成像技术是实现相机小型化、提高相机空间分辨率的有效方法。

The concept of mixed pixel, development situation, and the linear mixing spectral model and their resolution method were briefly introduced .

介绍了混合像元的概念、研究现状和广泛用于混合像元分解的线性模型。

The result is compared with the result from BP, which shows that this method can efficiently resolve the problem of sub-pixel mapping .

结果证明，利用本文方法能够更有效地解决亚像元定位的问题。

The fractional abundances of the selected end members at all the pixels are then computed using linear spectral unmixing method.

基于线性光谱混合模型求各像元对应于端元向量的丰度值；

The remotely sensed image often has many mixed -pixels , which is one of the important reasons for low interpretation accuracy.

由于传感器的分辨率的限制，在低空间分辨率遥感图像中存在着大量的混合像元。

A sub-pixel analysis algorithm using Bayesian network was proposed to deal with images with insufficient spectral information .

提出了基于贝叶斯网络的混合像元分解模型来处理光谱信息不足的遥感影像。

Coefficients between vegetation indices of different pixel scale and LAI respective were got by correlation analysis in different periods .

通过相关分析，得到不同时期不同像元尺度植被指数与 LAI 的相关系数。

The problems of pixel mixture are the main obstacles of quantitative remote sensing .

混合像元问题是定量遥感的主要障碍之一。

The experiment results show that the method can provide effective re-suit of abundance estimation for hyperspectral image .

实验结果表明，该方法能较好地估计出混合像元的组分比。

And the relationship between constrained least square unmixed accuracy and within endmember variances was analyzed with simulation data .

然后用数值模拟的方式进一步研究了端元内部光谱与限定性混合像元分解精度的关系。

Those mixed mixed decomposition methods given before are impractiable, or not suitable for AVHRR data .

已有的混合像元分解法要么实际操作困难，要么不适用于 AVHRR 数据。

In addition , mixture pixel analysis fails to account for the spatial distribution of class proportions within the pixel .

此外，混合像元分解无法刻画像元内部组分值的空间分布。

After the experimental data processing , a function of pixel number -wavelength was established.

通过对仪器的输出结果进行数据处理，得到了像元序号与波长的函数表达式。

The simulated results show that the different geometric sh apes of pixel affect the MTF of CCD in different degree .

模拟结果表明：CCD 像元的几何形状对 MTF 有不同程度的影响。

A new method for the decomposition of mixed pixels of multi-channel remote sensing images is proposed .

提出一种新的对多通道遥感图像进行混合像元分解的方法。

The results show that the dimidiate pixel model based on NDVI can ha used in the extraction of vegetation coverage in desertified region .

研究结果表明，基于 NDVI 的像元二分模型适合于沙漠化地区的植被覆盖度提取。

In order to describe the parameters of detector accurately , especially the non-uniformity, the radiation flux on pixel need to be corrected .

为了真实地反映探测器的性能参数，特别是非均匀性，需要对像元辐射通量进行校正。

To improve the accuracy of Medium-Resolution image in urban studies , we must solve the problem of decomposition for the mixed pixel .

为提高中分辨率遥感影像在城市研究中的应用，必须解决混合像元分解的问题。

Some causes of CCD pixel non-uniformity are theoretically analyzed .

从理论上分析了 CCD 像元光电转换率不一致性产生的多种原因。

Therefore, pixel unmixing is essential to distraction information on mineral type .

要提取物质类型的含量信息，必须进行混合像元分解。

The calibration accuracy is less than 0 . 5 pixel , which conforms to requirements .

本次校正精度小于 0.5 个像元，符合要求。

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