辐射定标英文

1、ETM+(Enhanced Thematic Mapper)是增强型专题制图仪. 1999年4月15日美国航空航天局发射了Landsat-7卫星，以保持地球图像、全球变化的长期连续监测。传感器是增强型专题绘图仪 ETM(EarthThematicMappe:)，星上设绝对定标，提高了对地观测分辨率和定位质量，调整了辐射测量精度、范围和灵敏度，通过增益减少了强反射体造成的高亮度饱和效应。该设备增加了一个15m分辨率的全色波段，热红外通道的空间分辨率也提高了一倍，达到了60m，每一景覆盖面积:185km*170km，重叠率:赤道上相邻两景图像旁向重叠率7.3%，轨道方向重叠率为5%，band6分别具有高、低增益两种图像数据，bandl-5、7增益随季节变化可调整(主要参数见表右上)。 2、ETM(Electronic technology manual)电子技术手册； 3、ETM(Embedded Trace Macrocell）内置追踪宏单元。 4、ETM(Experiment Teaching Management)实验教学管理。 5、ETM （EduTainMent ）是国内玩具加盟连锁店品牌，爱就推门的英文简称。 6、ETM（Embedded Transmission Module ）嵌入式传输模块 7、ETM（Entrance Test for Master's programs）研究生入学考试 8、ETM(Extreme trubulence model)极端湍流模型 9、ETM生态湿地处理系统

中文名称，改成英文名称

Spectral Enhancement（多光谱变换）The techniques require more than one band of data.处理思路（英文讲义上未解释）：辐射增强和空间增强对一个波段或其局部进行变换，多光谱变换则对多波段图像的整体进行变换。具体方法与GIS课中所讲空间统计分析类似。假定有n个变量、m个样点数据，则可组成一个n行、m列的数据矩阵：具有各自地理位置的 样点1 样点2 …… 样点j …… 样点m空间变量 X1X2…Xi …Xn x11 x12 …… x1j …… x1mx21 x22 …… x2j …… x2m… xi1 xi2 …… xij …… xim …xn1 xn2 …… xnj …… xnm同样，我们这里将每一波段视为一维变量，将多波段图像视为多维空间，采用多维空间线性变换的方法，形成新的多维变量，即新的多波段图像。假定图像有m波段，每波段K列L行，共n个像元（n = KL）则m波段图像组成m维向量矩阵：不少人对多光谱变换概念模糊就是因为将此矩阵与图像本身矩阵相混淆，多光谱空间概念对下章讲分类提取也很重要。Spectral Enhancement 就是利用一个变换矩阵A，将原图像（矩阵X）变换为一个新的多维变量或多波段图象（矩阵Y）。即：Y = AX，或下面讲述几种多光谱变换的方法Principal Components Analysis (PCA，主成分分析)This is often used as a method of data compression. It allows redundant data to be compacted into fewer bands—that is, the dimensionality of the data is reduced. The bands of PCA data are noncorrelated and independent, and are often more interpretable than the source data.补充：有关统计概念 nVariance = [ ∑(xip-mxi) 2 ] /n-1（第i波段第p像元） P=1方差是信息丰富程度的一种度量。 nCovariance = [ ∑(xip-mxi) (xjp-mxj) ]/n-1 P=1协方差能体现变量间相似性或重复性，是信息冗余程度的一种度量。为此，协方差矩阵与直方图一样，成为图像处理中的一个基本分析数据和工具。COV = 式中,V ij为第 I 波段与第 j 波段之间的协方差。例：TM图像fj2的协方差矩阵为 1 2 3 4 5 6 71 87.431 50.610 84.522 18.195 96.021 35.029 80.2022 50.610 32.399 53.773 23.158 74.913 22.151 54.7733 84.522 53.773 93.785 30.101 127.842 36.951 95.8054 18.195 23.158 30.101 183.716 191.943 20.116 65.0465 96.021 74.913 127.842 191.943 389.152 62.380 198.1046 35.029 22.151 36.951 20.116 62.380 27.646 43.3807 80.202 54.773 95.805 65.046 198.104 43.380 127.426问题：哪波段信息量最大（小）？哪两波段间信息冗余最大（小）？主成分分析旨在寻求一个矩阵变换Y = AX，使所得新图像Y的协方差矩阵为：COV = 式中，V11 > V 22 >…… > V mn，即新图像Y的各波段间的协方差为零，集中了图像大多数方差的前几个波段，分别称为PC1（第一主成分）、PC2、PC3…线性代数表明，以m个特征向量组成的mm方阵，作为变换矩阵A，即可达到上述目的，且变换中新旧图像Y和X的方差总量不变。例如：上面给出协方差矩阵的TM图像fj2，经主成分变换后形成新的图像fj2_pc，其协方差矩阵为703.318 0 0 0 0 0 00 174.407 0 0 0 0 00 0 43.816 0 0 0 00 0 0 11.242 0 0 00 0 0 0 4.652 0 00 0 0 0 0 3.459 00 0 0 0 0 0 0.661几何意义：线性变换——坐标轴平移或旋转。The process is easily explained graphically with an example of data in two bands. （讲解Page 153 - 155）评论：数学的力量。反过来，图像处理加深对数学的理解。PC方法常用于与其它方法相结合，如前面的Crisp和Resolution Merge。英语总结：The different bands in a multispectral image can be visualized as defining an N-dimensional space where N is the number of bands. Each pixel, positioned according to its DN value in each band, lies within the N-dimensional space. This clustering of the pixels is termed the data structure.The data structure can be considered a multidimensional hyperellipsoid. The principal axes of this data structure are not necessarily aligned with the axes of the data space. They are more directly related to the absorption spectra. You could view the axes that are largest for the data structure produced by the absorption peaks of special interest for a application.For example, a geologist and a botanist are interested in different absorption features. They would want to view different data structures and therefore, different data structure axes. Both would benefit from viewing the data in a way that would maximize visibility of the data structure of interest.Tasseled Cap（缨帽变换）The Tasseled Cap transformation offers a way to optimize data viewing for vegetation studies. Research has produced three data structure axes that define the vegetation information content.数学统计上最优的Principal Components方法不一定对各种应用都最优。这里的Tasseled Cap也是一种多维线性变换，但它的坐标轴平移或旋转朝最有利于观测植被地物的方向进行平移或旋转。• Brightness—a weighted sum of all bands, defined in the direction of the principal variation in soil reflectance.• Greenness—orthogonal to brightness, a contrast between the near-infrared and visible bands. Strongly related to the amount of green vegetation in the scene.• Wetness—relates to canopy and soil moisture.These rotations are sensor-dependent。For TM4 (第页). 缨帽变换系数的分析RGB to IHS 及其逆计算It is possible to define an alternate color space that uses intensity (I), hue (H), and saturation (S). This system is advantageous in that it presents colors more nearly as perceived by the human eye.雷达图像的应用。IndicesIndices are used to create output images by mathematically combining the DN values of different bands(波段间的algebra，P180).Applications• Indices are used extensively in mineral exploration and vegetation analysis to bring out small differences between various rock types and vegetation classes. • Indices can also be used to minimize shadow effects in satellite and aircraft multispectral images. Black and white images of individual indices or a color combination of three ratios may be generated.Integer Scaling Considerations由于波段比值可能变动很大，计算结果重新定标取整问题在这里特别突出。Index Examples （P182-183）（回顾第一章的Figure 1-6图，帮助理解植被指数等。）Hyperspectral Image ProcessingHyperspectral image processing is, in many respects, simply an extension of the techniques used for multispectral data sets; indeed, there is no set number of bands beyond which a data set is hyperspectral. Thus, many of the techniques or algorithms currently used for multispectral data sets are logically applicable. What is of relevance in evaluating these data sets is not the number of bands per se, but the spectral bandwidth of the bands (channels). As the bandwidths get smaller, it becomes possible to view the data set as an absorption spectrum rather than a collection of discontinuous bands. Analysis of the data in this fashion is termed imaging spectrometry.A hyperspectral image data set is recognized as a three-dimensional pixel array（Figure 65）（6-23）.A data set with narrow contiguous bands can be plotted as a continuous spectrum and compared to a library of known spectra. A serious complication in using this approach is assuring that all spectra are corrected to the same background.At present, it is possible to obtain spectral libraries of common materials. The JPL and USGS mineral spectra libraries are included in ERDAS IMAGINE. These are laboratory-measured reflectance spectra of reference minerals, often of high purity and defined particle size. The spectrometer is commonly purged（净化） with pure nitrogen to avoid absorbance by atmospheric gases. Conversely, the remote sensor records an image after the sunlight has passed through the atmosphere (twice) with variable and unknown amounts of water vapor, CO2. The unknown atmospheric absorbances superimposed upon the Earth’s surface reflectances makes comparison to laboratory spectra or spectra taken with a different atmosphere inexact. Indeed, it has been shown that atmospheric composition can vary within a single scene. This complicates the use of spectral signatures even within one scene. A number of approaches have been advanced to help compensate for this atmospheric contamination of the spectra. Fourier AnalysisImage enhancement techniques can be divided into two basic categories: point and neighborhood. Point techniques enhance the pixel based only on its value, with no concern for the values of neighboring pixels. These techniques include contrast stretches (nonadaptive), classification, and level slices. Neighborhood techniques enhance a pixel based on the values of surrounding pixels. As a result, these techniques require the processing of a possibly large number of pixels for each output pixel. The most common way of implementing these enhancements is via a moving window convolution. However, as the size of the moving window increases, the number of requisite calculations becomes enormous. An enhancement that requires a convolution operation in the spatial domain can be implemented as a simple multiplication in frequency space—a much faster calculation.In ERDAS IMAGINE, the FFT is used to convert a raster image from the spatial (normal) domain into a frequency domain image. The FFT calculation converts the image into a series of two-dimensional sine waves of various frequencies. The Fourier image itself cannot be easily viewed, but the magnitude of the image can be calculated, which can then be displayed either in the Viewer or in the FFT Editor. Analysts can edit the Fourier image to reduce noise or remove periodic features, such as striping. Once the Fourier image is edited, it is then transformed back into the spatial domain by using an IFFT. The result is an enhanced version of the original image.The basic premise（前提） behind a Fourier transform is that any one-dimensional function, f(x) (which might be a row of pixels), can be represented by a Fourier series consisting of some combination of sine and cosine terms and their associated coefficients.176页上的图72A Fourier transform is a linear transformation that allows calculation of the coefficients necessary for the sine and cosine terms to adequately represent the image. This theory is used extensively in electronics and signal processing. Therefore, DFT has been developed. Because of the computational load in calculating the values for all the sine and cosine terms along with the coefficient multiplications, a highly efficient version of the DFT was developed and called the FFT.DFT ——177上的公式，FFT 快速算法e-j2π(ax+by) = cos2π(ax+by) -jsin2π(ax+by)The raster image generated by the FFT calculation is not an optimum image for viewing or editing. Each pixel of a fourier image is a complex number (i.e., it has two components: real and imaginary). For display as a single image, these components are combined in a root-sum of squares operation（傅利叶变换的模作为像元值来显示 u,v图像）. Also, since the dynamic range of Fourier spectra vastly exceeds the range of a typical display device, the Fourier Magnitude calculation involves a logarithmic function. Finally, a Fourier image is symmetric about the origin (u, v = 0, 0). If the origin is plotted at the upper left corner, the symmetry is more difficult to see than if the origin is at the center of the image. Therefore, in the Fourier magnitude image, the origin is shifted to the center of the raster array.

etm是什么意思 ETM(Enhanced Thematic Mapper)是增强型专题绘图仪.美国陆地卫星6(LANDSAT-6)搭载的一种成像仪，可以到国外的网站上下载ETM和TM的遥感图像

流明，光通量的单位。发光强度为1坎德拉(cd)的点光源，在单位立体角（1球面度）内发出的光通量为“1流明”，英文缩写(lm)。希望对你有帮助O(∩_∩)O~

由于感性负荷电流滞后电压Φ角，所以进行功率计算时，需要把电流向量投影到电压向量方向上去，因此出现一个cosΦ，这个相位角Φ的余弦称为功率因数。 功率因数也可以是有功功率和视在功率的比值，即cosΦ=P/S。意义： ① 通过改善功率因数，减少了线路中总电流和供电系统中的电气元件，如变压器、电器设备、导线等的容量，因此不但减少了投资费用，而且降低了本身电能的损耗。 ② 藉由良好功因值的确保，从而减少供电系统中的电压损失，可以使负载电压更稳定，改善电能的质量。 ③ 可以增加系统的裕度，挖掘出了发供电设备的潜力。如果系统的功率因数低，那么在既有设备容量不变的情况下，装设电容器后，可以提高功率因数，增加负载的容量。 ④ 减少了用户的电费支出；透过上述各元件损失的减少及功率因数提高的电费优惠。在交流电路中，电压与电流之间的相位差(Φ)的余弦叫做功率因数，用符号cosΦ表示，在数值上，功率因数是有功功率和视在功率的比值，即cosΦ=P/S 功率因数的大小与电路的负荷性质有关， 如白炽灯泡、电阻炉等电阻负荷的功率因数为1，一般具有电感或电容性负载的电路功率因数都小于1。功率因数是电力系统的一个重要的技术数据。功率因数是衡量电气设备效率高低的一个系数。功率因数低，说明电路用于交变磁场转换的无功功率大， 从而降低了设备的利用率，增加了线路供电损失。所以，供电部门对用电单位的功率因数有一定的标准要求。 (1) 最基本分析：拿设备作举例。例如：设备功率为100个单位，也就是说，有100个单位的功率输送到设备中。然而，因大部分电器系统存在固有的无功损耗，只能使用70个单位的功率。很不幸，虽然仅仅使用70个单位，却要付100个单位的费用。在这个例子中，功率因数是0.7 (如果大部分设备的功率因数小于0.9时，将被罚款)，这种无功损耗主要存在于电机设备中(如鼓风机、抽水机、压缩机等)，又叫感性负载。功率因数是马达效能的计量标准。 (2) 基本分析：每种电机系统均消耗两大功率，分别是真正的有用功(叫千瓦)及电抗性的无用功。功率因数是有用功与总功率间的比率。功率因数越高，有用功与总功率间的比率便越高，系统运行则更有效率。 (3) 高级分析：在感性负载电路中，电流波形峰值在电压波形峰值之后发生。两种波形峰值的分隔可用功率因数表示。功率因数越 光通量的单位。发光强度为1坎德拉(cd)的点光源，在单位立体角（1球面度）内发出的光通量为“1流明”。英文缩写(lm)。所谓的流明简单来说，就是指蜡烛一烛光在一公尺以外的所显现出的亮度.一个普通40瓦的白炽灯泡，其发光效率大约是每瓦10流明，因此可以发出400流明的光. 40瓦的白炽灯220伏时，光通量为340流明。光通量是描述单位时间内光源辐射产生视觉响应强弱的能力，单位是流明，也叫明亮度。投影仪表示光通量的单位是ANSI流明，ANSI流明是美国国家标准化协会制定的测量投影仪光通量的标准，它测量屏幕"田"字形九个交叉点上的各点照度，乘以面积，再求九点的平均值，即为该投影仪的ANSI流明。流明值越高表示越亮，明亮度越高则在投影时就不需要关灯。 ANSI为American National Standards Institute（美国国家标准局）的缩写。同样，这个量是对光源而言，是描述光源发光总量的大小的，与光功率等价。光源的光通量越大，则发出的光线越多对于各向同性的光（即光源的光线向四面八方以相同的密度发射），则 F = 4πI。也就是说，若光源的I为1cd，则总光通量为4π =12.56 lm。与力学的单位比较，光通量相当于压力，而发光强度相当于压强。要想被照射点看起来更亮，我们不仅要提高光通量，而且要增大会聚的手段，实际上就是减少面积，这样才能得到更大的强度。要知道，光通量也是人为量，对于其它动物可能就不一样的，更不是完全自然的东西，因为这种定义完全是根据人眼对光的响应而来的。人眼对不同颜色的光的感觉是不同的，此感觉决定了光通量与光功率的换算关系。对于人眼最敏感的555nm的黄绿光，1W = 683 lm，也就是说，1W的功率全部转换成波长为555nm的光，为683流明。这个是最大的光转换效率，也是定标值，因为人眼对555nm的光最敏感。对于其它颜色的光，比如650nm的红色，1W的光仅相当于73流明，这是因为人眼对红光不敏感的原因。对于白色光，要看情况了，因为很多不同的光谱结构的光都是白色的。例如LED的白光、电视上的白光以及日光就差别很大，光谱不同。至于电光源的发光效率，是另外一个相关的话题，是说1W的电功率到底能转化成多少光通量。如果全部转换成555nm的光，那就是每瓦683流明。但如果有一半转换成555nm的光，另一半变成热量损失了，那效率就是每瓦341.5流明。白炽灯能达到1W=20 lm就很不错了，其余的都成为热量或红外线了。测量一个不规则发光体的光通量，要用到积分球，比较专业而复杂。大明互助团队真诚为您解答，祝您愉快！希望您在满意的答案上选择“采纳”☆⌒_⌒☆"!