Satellite(mlbench)
Satellite()所属R语言包：mlbench
Landsat Multi-Spectral Scanner Image Data
地球资源卫星多光谱扫描仪图像数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values.
该数据库包括在3x3的街区的卫星图像中的像素，并在每个邻近的中央像素与分类的多光谱的值。宗旨是预测此分类，给定的多光谱值。
用法----------Usage----------
data(Satellite)
格式----------Format----------
A data frame with 36 inputs (x.1 ... x.36) and one target (classes).
有36个输入数据框（x.1 ... x.36（classes））和一个目标。
Details
详细信息----------Details----------
One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels.
陆地卫星MSS图像的一帧由四个数字图像在不同的光谱带的同一场景。其中两个是在可见光区域（大致对应的可见光谱的绿色和红色区域）和两个中的（近）红外线。每个象素是一个8位的二进制字，0对应于黑色，255为白色。的像素的空间分辨率是约80m乘八十米。每个图像都包含2340 x 3380这样的像素。
The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel.
该数据库是（小）分区域的一个场景，其中包括82×100像素。的数据的每行对应于一个3x3完全包含在82x100子区域之内的像素的方形邻域。每一行中包含的四个光谱波段（转换为ASCII码）的每一个的9个像素中的3×3邻域和一个数字，指示的中心像素的分类标签中的像素值。
The classes are
类
The data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset.
给出的数据是随机的顺序和某些行的数据已被删除，所以你不能从这个数据集重建原始图像。
In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary.
左上角像素在每一行中的数据的4个频谱值是给定的第一随后的四个频谱顶部中间的像素值，然后那些右上方的像素，等等上的像素读出的顺序左到右，顶部至底部。因此，四个频谱属性17,18,19和20的中心像素的值由下式给出。如果你喜欢，你可以只使用这四个属性，而忽略了其他。这样就避免了出现的问题，一个的3x3邻跨越的边界时。
起源----------Origin----------
The original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for Remote Sensing, and used for research at: The Centre for Remote Sensing, University of New South Wales, Kensington, PO Box 1, NSW 2033, Australia.
该数据库的原始地球资源卫星数据产生的购买澳大利亚研究中心从美国航空航天局的遥感数据，用于研究在遥感中心，肯辛顿，邮政信箱：1，NSW 2033，澳大利亚新南威尔士大学， 。
The sample database was generated taking a small section (82 rows and 100 columns) from the original data. The binary values were converted to their present ASCII form by Ashwin Srinivasan. The classification for each pixel was performed on the basis of an actual site visit by Ms. Karen Hall, when working for Professor John A. Richards, at the Centre for Remote Sensing at the University of New South Wales, Australia. Conversion to 3x3 neighbourhoods and splitting into test and training sets was done by Alistair Sutherland.
该示例数据库生成以从原始数据中的一小部分（82行和100列）。他们目前的ASCII形式的二进制值转换为Ashwin斯里尼瓦桑。 ，澳大利亚新南威尔士大学遥感中心的实际工作时，约翰·A·理查兹教授，凯伦女士馆，实地考察的基础上，对每个像素进行分类。 3x3的街区和分裂成测试和训练集的转换是由Alistair萨瑟兰。
历史----------History----------
The Landsat satellite data is one of the many sources of information available for a scene. The interpretation of a scene by integrating spatial data of diverse types and resolutions including multispectral and radar data, maps indicating topography, land use etc. is expected to assume significant importance with the onset of an era characterised by integrative approaches to remote sensing (for example, NASA's Earth Observing System commencing this decade). Existing statistical methods are ill-equipped for handling such diverse data types. Note that this is not true for Landsat MSS data considered in isolation (as in this sample database). This data satisfies the important requirements of being numerical and at a single resolution, and standard maximum-likelihood classification performs very well. Consequently, for this data, it should be interesting to compare the performance of other methods against the statistical approach.
地球资源卫星数据是一个场景的许多可用的信息来源之一。通过整合不同类型和解决方案，包括空间数据的多光谱和雷达数据，表明地形的图场景的解释，土地使用等承担显着的重要性，一个时代的发病特点是中西医结合的方法，以遥感（例如，美国航空航天局的地球观测系统，这十年开始）。现有的统计方法，没有能力处理这些不同的数据类型。请注意，这是不正确的考虑隔离（在此示例数据库）的Landsat MSS数据。该数据的数值在一个单一的决议满足的重要需求，最大似然分类标准执行得很好。因此，对于此数据，它应该是有趣的性能进行比较，对统计方法的其他方法。
源----------Source----------
Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde, Glasgow, Scotland, UK, ross@uk.ac.turing
Ashwin斯里尼瓦桑，统计和数据建模，斯特拉斯克莱德大学，格拉斯哥，苏格兰，英国，ross@uk.ac.turing
These data have been taken from the UCI Repository Of Machine Learning Databases at
这些数据已经从UCI机器学习数据库存储库在
ftp://ftp.ics.uci.edu/pub/machine-learning-databases
http://www.ics.uci.edu/~mlearn/MLRepository.html
and were converted to R format by Friedrich Leisch.
被转换为R格式由Friedrich Leisch。
参考文献----------References----------
UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
实例----------Examples----------
data(Satellite)
summary(Satellite)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-9-23 12:27:49
