求The ERDAS Field Guide第一章内容PrefaceThe ERDAS Field Guide is now being used as a textbook, lab manual, and training guide throughout the world.The ERDAS Field Guide will continue to expand and improve to keep pace with the profession.谢谢

求The ERDAS Field Guide第一章内容PrefaceThe ERDAS Field Guide is now being used as a textbook, lab manual, and training guide throughout the world.The ERDAS Field Guide will continue to expand and improve to keep pace with the profession.谢谢
求The ERDAS Field Guide第一章内容
Preface
The ERDAS Field Guide is now being used as a textbook, lab manual, and training guide throughout the world.
The ERDAS Field Guide will continue to expand and improve to keep pace with the profession.
谢谢

求The ERDAS Field Guide第一章内容PrefaceThe ERDAS Field Guide is now being used as a textbook, lab manual, and training guide throughout the world.The ERDAS Field Guide will continue to expand and improve to keep pace with the profession.谢谢
Chapter 2 Vector Layers
本章教学要求：有关专业英语单词
Introduction
ERDAS IMAGINE is designed to integrate two data types, raster and vector, into one system. The vector data structure in ERDAS IMAGINE is based on the ArcInfo data model (developed by ESRI, Inc. ). This chapter describes vector data, attribute information, and symbolization.
You can use ArcInfo coverages directly without importing them.
记住 Figure 2-1: Vector Elements 中的英文词.
ArcGIS Integration
ArcGIS Integration is the method you use to access the data in a geodatabase. ERDAS IMAGINE has always supported ESRI data formats such as coverages and shapefiles, and now, using ArcGIS Vector Integration, ERDAS IMAGINE can also access CAD and VPF data on the internet.
Chapter 3
Raster and Vector Data Sources
本章教学要求：1、重点：Satellite data部分
2其余部分,仅要求标题有关专业英语单词
Introduction
This chapter is an introduction to the most common raster and vector data types that can be used with the ERDAS IMAGINE software package.
The raster data types covered include: （See text,
Importing and Exporting（不用理会)
Satellite Data
There are several data acquisition options available including photography, aerial sensors, and sophisticated satellite scanners. However, a satellite system offers these advantages:
• Easily processed and analyzed by a computer.
• Many satellites orbit the Earth, so the same area can be covered on a regular basis for change detection.
• Once the satellite is launched, the cost for data acquisition is less than that for aircraft data.
• Satellites have very stable geometry, meaning that there is less chance for distortion or skew in the final image.
Satellite System
A satellite system is composed of a scanner with sensors and a satellite platform. The sensors are made up of detectors.
（See the detailed in text）
Satellite Characteristics
The U. S. Landsat and the French SPOT satellites are two important data acquisition satellites. They have several characteristics in common:
• Both scanners can produce nadir views.
• They have sun-synchronous orbits, meaning that they rotate around the Earth at the same rate as the Earth rotates on its axis, so data are always collected at the same local time of day over the same region.
• They both record electromagnetic radiation in one or more bands. Multiband data are referred to as multispectral imagery. Single band, or monochrome, imagery is called panchromatic.
Figure 3-1 Multispectral Imagery Comparison
IKONOS（was launched in September of 1999）
The resolution of the panchromatic sensor is 1 m. The resolution of the multispectral scanner is 4 m. The swath width is 13 km at nadir.
Table 3-4: IKONOS Bands and Wavelengths
补：美DigitalGlobel (EarthWatch)公司的QuickBird（快鸟）图像,波段分布与IKONOS同,但pan和multispectral分辨率分别达0.6和2.4米,为民用卫星之最.
IRS (Indian Remote Sensing Satellite)
Landsat 1-5 (See the histoty in text)
Landsats 1, 2, and 3 are no longer operating, but Landsats 4 and 5 are still in orbit gathering data.
Landsats 1, 2, and 3 gathered Multispectral Scanner (MSS) data and Landsats 4 and 5 collect MSS and TM data.
MSS (Multispectral Scanner)
MSS data are widely used for general geologic studies as well as vegetation inventories.
Table 3-8: MSS Bands and Wavelengths
TM （Thematic Mapper）
The TM scanner is a multispectral scanning system much like the MSS. TM has higher spatial, spectral, and radiometric resolution than MSS.
The spatial resolution of TM is 28.5 × 28.5 m for all bands except the thermal (band 6), which has a spatial resolution of 120 × 120 m.
Table 3-9: TM Bands and Wavelengths
Landsat 7
launched in 1999, uses Enhanced Thematic Mapper Plus (ETM+) to observe the Earth.
Table 3-10: Landsat 7 Characteristics
NLAPS
NOAA Polar Orbiter Data
AVHRR
See Table 3-11: AVHRR Bands and Wavelengths, also Figure 3-1.
OrbView-3 (US) 类似 IKONOS.
SeaWiFS（Sea-viewing Wide Field-of-View Sensor）
SPOT
The sensors operate in two modes, multispectral（20m） and panchromatic (10m).
Also see Figure 3-1.
Panchromatic
XS (see table 3-14: SPOT XS Bands and Wavelengths)
SPOT 4 (was launched in 1998)
增加一个1.58 to 1.75 μm的近红外波段；See table 3-15.
补：SPOT 5—— Panchromatic 波段达2.5米分辨率.
补：我国资源一号卫星CBERS
Radar Data
Researchers are finding that a combination of the characteristics of radar data and visible/infrared data is providing a more complete picture of the Earth. In the last decade, the importance and applications of radar have grown rapidly.
Advantages of Using Radar Data
• Radar microwaves can penetrate the atmosphere day or night under virtually all weather conditions. 全天候
• Under certain circumstances, radar can partially penetrate arid and hyperarid surfaces, revealing subsurface features of the Earth.
• For research on bodies of water.
Radar Sensors
Radar images are generated by two different types of sensors:
SLAR (Figure 3-4)
SAR — uses a side-looking, fixed antenna to create a synthetic aperture.
Active and Passive Sensors
An active radar sensor gives off a burst of coherent radiation that reflects from the target, unlike a passive microwave sensor which simply receives the low-level radiation naturally emitted by targets.
Applications for Radar Data
• Geology
• Classification
• Glaciology
• Oceanography
• Hydrology
• Ship monitoring
• Offshore oil activities
• Pollution monitoring
Image Data from Aircraft
This is useful if there is not time to wait for the next satellite to pass over a particular area, or if it is necessary to achieve a specific spatial or spectral resolution that cannot be attained with satellite sensors.
GPS Data
Chapter 4 Image Display
本章教学要求：1、重点：RGB & Displaying Raster Layers 部分
2 、Using the Viewer 部分,结合实习1
Introduction
This section defines some important terms that are relevant to image display. This may differ from other systems, such as Microsoft Windows NT.
The display hardware contains the memory that is used to produce the image. This hardware determines which types of displays are available (e.g., true color or pseudo color) and the pixel depth (e.g., 8-bit or 24-bit).
Display Memory Size
• display resolution—the number of pixels that can be viewed on the display screen.
• the number of bits for each pixel or pixel depth.
Pixel（file pixel & display pixel）
• the data file value(s) for one data unit in an image
• one grid location on a display or printout
To display an image, a file pixel that consists of one or more numbers must be transformed into a display pixel with properties that can be seen, such as brightness and color.
Colors（RGB）
Red, green, and blue can be added together to produce a wide variety of colors, are therefore the additive primary colors.
（三原色和三补色,及其它颜色特性,推荐阅读彭书 58-66页）
color guns
On a display, color guns direct electron beams that fall on red, green, and blue phosphors. The phosphors glow at certain frequencies to produce different colors.
The combination of the three color guns, each with 28 possible brightness values, yields 224 or 16,777,216 possible colors for each pixel on a 24-bit display. (line 7-9, page 110)
Colormap and Colorcells
A colormap is an ordered set of colorcells, which is used to perform a function on a set of input values. To display or print an image, the colormap translates data file values in memory into brightness values for each color gun.
（SEE Table 4-1）
Colorcells
There is a colorcell in the colormap for each data file value. he red, green, and blue values assigned to the colorcell control the brightness of the color guns for the displayed pixel.
Colormap vs. Lookup Table
The colormap is a function of the display hardware, whereas a lookup table is a function of ERDAS IMAGINE.
Display Types
• 8-bit PseudoColor
• 24-bit DirectColor
• 24-bit TrueColor
32-bit Displays
A 32-bit display is a combination of an 8-bit PseudoColor and 24-bit DirectColor, or TrueColor display.
8-bit PseudoColor： a colormap with 256 colorcells.
This display grants a small number of colors to ERDAS IMAGINE. It works well with thematic raster layers containing less than 200 colors and with gray scale continuous raster layers. For image files with three continuous raster layers (bands), the colors are severely limited.
24-bit DirectColor or 24-bit TrueColor
两种方式（Colorcell or color gun）达到24位彩色实时显示的目的：
enables you to view up to three continuous raster layers (bands) of data at one time, creating displayed pixels that represent the relationships between the bands by their colors.
PC Displays
ERDAS IMAGINE for Microsoft Windows NT supports the following visual type and pixeldepths:
• 8-bit PseudoColor
• 24-bit TrueColor
8-bit PseudoColor
An 8-bit PseudoColor display for the PC uses the same type of colormap as the X Windows 8-bit PseudoColor display.
24-bit TrueColor
A 24-bit TrueColor display for the PC assigns colors the same way as the X Windows 24-bit TrueColor display.
Displaying Raster Layers
Continuous Raster Layers
An image file (.img) can contain >3 continuous raster layers; Therefore, when displaying an image file with continuous raster layers, it is possible to assign which layers (bands) are to be displayed with each of the three color guns.
Band assignments are often expressed in R,G,B order. E.g.
• Landsat TM—natural color: 3, 2, 1
• Landsat TM—color-infrared: 4, 3, 2
• SPOT Multispectral—color-infrared: 3, 2, 1

Contrast Stretch
Since the data file values in a continuous raster layer often represent raw data, the range of data file values is often small. Therefore, a contrast stretch is usually performed,...

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Contrast Stretch
Since the data file values in a continuous raster layer often represent raw data, the range of data file values is often small. Therefore, a contrast stretch is usually performed, which stretches the range of the values to fit the range of the display.
See figure 4-5.
statistics Files
To perform a contrast stretch, certain statistics are necessary, such as the mean and the standard deviation of the data file values in each layer.
（这些statistics变换下章将完整分析，这里只涉及有关图像显示的部分。这里仅要求观察图像的statistics，e.g. the mean, standard deviation, middle, maximum, minimum）
Figure 4-7 illustrates the general process of displaying continuous raster layers on a 24-bit TureColor display.
Thematic Raster Layers
A thematic raster layer generally contains pixels that have been classified. It is stored in an image (.img) file. Only one data file value (the class value) is stored for each pixel. The class system gives the thematic layer a discrete look, in which each class can have its own color.
即：为每种类别指定一种RGB的组合（颜色），指定表称为Color scheme.
Color Table
When a thematic raster layer is displayed, ERDAS IMAGINE automatically creates a color table. The red, green, and blue brightness values for each class are stored in this table.
RGB Colors
Using the Viewer (未纳入教材，但对实习有指导意义)
意义：基于内存快速显示和进行某些图像处理（不保存）。
The Viewer not only makes digital images visible quickly, but it can also be used as a tool for image processing and raster GIS modeling.
Chapter 5 Mosaic
Introduction
The Mosaic process offers you the capability to stitch images together so one large, cohesive image of an area can be created. Because of the different features of the Mosaic Tool, you can smooth these images before mosaicking them together as well as color balance them, or adjust the histograms of each image in order to present a better large picture.
某些Mosaic Tool涉及到下章Enhancement的知识。
Chapter 6 Enhancement
重点：Radiometric Enhancement, Spatial Enhancement and Spectral Enhancement
Introduction
Image enhancement is the process of making an image more interpretable for a particular application. The techniques are often used instead of classification techniques for feature extraction.
Display vs. File Enhancement
Image enhancement may be performed:
• temporarily, upon the image that is displayed in the Viewer
• permanently, upon the image data in the data file.
Enhancing a displayed image is much faster than enhancing an image on disk.
Spatial Modeling Enhancements（基于组件应用建模）
• Graphical models（实习中有练习）
Correcting Data
Each generation of sensors shows improved data acquisition and image quality over previous generations. However, some anomalies still exist that are inherent to certain sensors and can be corrected by applying mathematical formulas derived from the distortions. In addition, the natural distortion that results from the curvature and rotation of the Earth in relation to the sensor platform produces distortions in the image data, which can also be corrected.
Generally, there are two types of data correction: radiometric and geometric.
这些辐射改正和几何改正（非10章之精校正）通常由卫星接收站完成。
Radiometric Enhancement（辐射增强）
Radiometric enhancement deals with the individual values of the pixels. It differs from spatial enhancement (discussed in “Spatial Enhancement”), which takes into account the values of neighboring pixels.
Radiometric enhancements that are applied to one band may not be appropriate for other bands.
简明地说：辐射增强用不涉及像元邻域性的统一变换关系来改变某个波段的 pixel value，。
Histograms concept（我加的小标题）
横坐标：pixel value（0-255）；
纵坐标：某像元值出现的Frequency
直方图是图像最基本的统计数据，它体现图的“调子”（明暗色调反差）。
Contrast Stretching
When radiometric enhancements are performed on the display device, the transformation of data file values into brightness values is illustrated by the graph of a lookup table.
Histograms和graph of a lookup table是分析辐射增强最有用的两种图。
Linear and Nonlinear
The terms linear and nonlinear, when describing types of spectral enhancement, refer to the function that is applied to the data to perform the enhancement. A piecewise linear stretch uses a polyline function to increase contrast to varying degrees over different ranges of the data, as in Figure 6-3.
Linear Contrast Stretch
Figure 4-5 就是用一种线性变换来进行反差拉伸。
注意149页底部有ERDAS提示：A two standard deviation linear contrast stretch is automatically applied to images displayed in the Viewer.
Histogram Equalization（直方图均衡化）
A nonlinear stretch that redistributes pixel values so that there is approximately the same number of pixels with each value within a range. The result approximates a flat histogram.
See figure 6-7
（补充：方法来源：累积直方图曲线作为变换关系。）
The resulting histogram is not exactly flat. 因为实际制作Histogram Equalization软件，是用差分代替微分（——教材中的Bin）
又：评论：最增强反差的方法，但并不等于效果最好。
Histogram Matching（直方图匹配）
Histogram matching is the process of determining a lookup table that converts the histogram of one image to resemble the histogram of another. Histogram matching is useful for matching data of the same or adjacent scenes that were scanned on separate days, or are slightly different because of sun angle or atmospheric effects. This is especially useful for mosaicking or change detection（后者指比较同区域两图以发现变化）.
To achieve good results with histogram matching, the two input images should have similar characteristics:
• The general shape of the histogram curves should be similar.
• Relative dark and light features in the image should be the same.
• For some applications, the spatial resolution should be the same.
• The relative distributions of land covers should be about the same.
（补充：方法来源：用二者的累积直方图曲线两次变换）
Brightness Inversion
The brightness inversion functions produce images that have the opposite contrast of the original image.
Spatial Enhancement（空间增强）
While radiometric enhancements operate on each pixel individually, spatial enhancement modifies pixel values based on the values of surrounding pixels.
Spatial enhancement deals largely with spatial frequency, which is the difference between the highest and lowest values of a contiguous set of pixels.
See Figure 6-11
Convolution Filtering (卷积过滤)
The process of averaging small sets of pixels across an image. It is used to change the spatial frequency characteristics of an image.
A convolution kernel(卷积模板) is a matrix that is used to average the value of each pixel with the values of surrounding pixels in a particular way. The numbers (often called coefficients) in the matrix serve to weight this average toward particular pixels.
Convolution filtering is one method of spatial filtering.
See Figure 6-12
convolution相当于上学期GIS课中讲过的Moving windows 活动窗口法之移动平均法。
下面讲三种类型的Kernel:
Low-Frequency or low-pass Kernels
(讲义上放在后的第三种Kernels，这里提到前面先讲 )
This kernel simply averages the values of the pixels, causing them to be more homogeneous. The resulting image looks either more smooth or more blurred, decreases spatial frequency.
（补充：两种常用的 Low-Frequency Kernels——均值滤波和中值滤波，前者即滑动平均，后者取模板覆盖的像元值大小排列的中间像元的值。利用中值滤波可滤掉某些像元值异常的点。）
Zero-Sum Kernels
Zero-sum kernels are kernels in which the sum of all coefficients in the kernel equals zero.
This generally causes the output values to be:
• zero in areas where all input values are equal (no edges)
• low in areas of low spatial frequency
• extreme in areas of high spatial frequency
Therefore, a zero-sum kernel is an edge detector.
The resulting image often consists of only edges and zeros.
几种常用的 Zero-Sum Kernels例：
罗伯特算子： 索伯尔算子 拉普拉斯算子
Zero-sum kernels can be biased to detect edges in a particular direction.
其它方向的探测，如：
High-Frequency or high-pass Kernels
It has the effect of increasing spatial frequency and serves as edge enhancers. Unlike edge detectors, they highlight edges and do not necessarily eliminate other features.
When this kernel is used on a set of pixels, the relative low value gets lower, the relative high value becomes higher.
See example of text (page 160-161) .
补充: How to derove high-pass Kernels? ——某种算子与原图叠加
1、原图“减”拉普拉斯算子
拉普拉斯算子：(左-自)-（自-右）+(上-自)-（自-下）
即：
原图减拉普拉斯算子：
2、原图 + 原图减滑动平均
两原图减滑动平均：
Crisp
The Crisp filter sharpens the overall scene luminance without distorting the interband variance content of the image. This is a useful enhancement if the image is blurred.
在后面讲解主成分变换（Principal Component or PC）方法后回头来理解此段。
Wavelet Resolution Merge
low spatial resolution to be sharpened using a co-registered panchromatic image of relatively higher resolution. A primary intended target dataset is Landsat 7 ETM+. Increasing the spatial resolution of multispectral imagery in this fashion is, in fact, the rationale behind the Landsat 7 sensor design.
Aside from trad