ENVI对SAR数据的预处理过程(详细版)
ENVI Sarscape入门教程V1.1
基于ENVI的SAR数据处理流程介绍
ENVI Tutorial:Basic SAR Processing andAnalysisTable of ContentsO VERVIEW OF T HIS T UTORIAL (2)Background (2)S INGLE-B AND SAR P ROCESSING (3)Read and Display RADARSAT CEOS Data (3)Review CEOS Header (3)Apply Square-Root Contrast Stretch (4)Remove Speckle using Adaptive Filters (5)Density Slice (6)Edge Enhancement (7)Data Fusion (8)Image-Map Output (9)Overview of This TutorialThis tutorial is designed to give you a working knowledge of ENVI’s basic tools for processing single-band synthetic aperture radar (SAR) data such as RADARSAT, ERS-1, and JERS-1.Files Used in This TutorialENVI Resource DVD: envidata\rsat_subFile Descriptionlea_01.001 RADARSAT leader filebonnrsat.img (.hdr) RADARSAT image subsetrsi_f1.img (.hdr) Frost filter resultdslice.dsr Density slice filersi_f2.img (.hdr) Laplacian filter resultrsi_f3.img (.hdr) Laplacian filter result with 90% add-backrsi_fus.img (.hdr) Simulated fused TM and RADARSATrsi_map.jpg RADARSAT map composition exampleBackgroundUse the Radar menu in ENVI to access standard and advanced tools for analysis of detected radar images and advanced SAR systems such as NASA/Jet Propulsion Laboratory's (JPL's) fully polarimetric AIRSAR and SIR-C systems. ENVI can process ERS-1, JERS-1, RADARSAT, SIR-C, X-SAR, and AIRSAR data and any other detected SAR dataset. In addition, ENVI is designed to handle radar data distributed in the CEOS format.Most standard ENVI processing functions are inherently radar-capable, including all display capabilities, stretching, color manipulations, classification, registration, filters, geometric rectification, and so on. Additional specialized tools are provided for analyzing polarimetric radar data. A typical processing flow may include reviewing the CEOS header, reading the CEOS data, displaying and contrast stretching, removing speckle using an adaptive filter, density slicing, edge enhancement, data fusion, and map composition.Single-Band SAR ProcessingThis section describes a typical single-band SAR processing scenario from data input through processing and analysis, to publication-quality or map output. You will use a subsetted RADARSAT 1 Path Image, Fine Beam 2, from December 17, 1995, Bonn, Germany.Read and Display RADARSAT CEOS DataENVI provides the tools to read generic CEOS data tapes and RADARSAT data from both tape and CD-ROM. To read data from tape, select File → Tape Utilities → Read Known Tape Formats → RADARSAT CEOS. To read original RADARSAT data from disk or CD, select Radar → Open/Prepare Radar File → RADARSAT. For this tutorial, a RADARSAT image subset has already been extracted.1.From the ENVI main menu bar, select File→Open Image File. A file selection dialog appears.2.Navigate to envidata\rsat_sub and select bonnrsat.img. Click Open.3.In the Available Bands List, select the Gray Scale radio button and click Load Band. The following figure showsthe subsetted RADARSAT image of Bonn, Germany, with a 2% linear stretch applied. These data were acquired during the RADARSAT commissioning phase and should not be used for scientific analysis or interpretation. Data are copyright, RADARSAT, 1995.Review CEOS HeaderMany SAR datasets are distributed in CEOS format. ENVI provides generic tools to read CEOS headers and display CEOS header information on the screen. ENVI also has tools specifically designed to read RADARSAT CEOS headers, which contain additional information.1.From the ENVI main menu bar, select Radar→Open/Prepare Radar File→View RADARSAT Header. Afile selection dialog appears.2.Select the RADARSAT leader file lea_01.001. A CEOS Header Report dialog appears.3.Browse the information in the CEOS Header Report, then close the dialog when you are finished.Apply Square-Root Contrast StretchRadar data typically cover a large range of data values. As seen above, default linear stretches do not perform a very good job of enhancing the contrast in most radar images. ENVI’s square-root stretch spreads out radar data over a given range of gray scales better than other types of stretches, resulting in an improved display of radar images.1.From the Display group menu bar, select Enhance→[Image] Square Root. The stretch is applied based onthe statistics of the data in the Image window. The following figure shows a square-root stretch of the BonnRADARSAT image. Compare to the linear contrast stretch above.2.In the Available Bands List, click Display #1 and select New Display. Click Load Band again to display theimage with the default 2% linear stretch.3.From a Display group menu bar, select Tools→Link→Link Displays. Click OK to link the square-root imagewith the 2% linear stretch image. Click in an Image window to toggle between the two images.Remove Speckle using Adaptive FiltersAdaptive filters remove radar speckle from images without seriously affecting the spatial characteristics of the data. The Frost-filtered image shown below is a considerable improvement over the unfiltered data. The Frost filter, an exponentially damped, circularly symmetric filter that uses local statistics, is used to reduce speckle while preserving edges in the data. The pixel being filtered is replaced with a value calculated based on the distance from the filter center, the damping factor, and the local variance.1.From the ENVI main menu bar, select Radar→Adaptive Filters→Frost. A Frost Filter Input File dialogappears.2.Select bonnrsat.img and click OK. A Frost Filter Parameters dialog appears.e the default Filter Size (3x3) and Damping Factor (1.0). Enter an output filename and click OK.4.In the Available Bands List, select the new Frost-filtered image and click Load Band. Or, load the pre-generatedfile rsi_f1.img to a new display group and apply a square-root stretch.5.From a Display group menu bar, select Tools→Link→Link Displays. Click OK to link the Frost-filtered imagewith the 2% linear stretch image. Click in an Image window to toggle between the two images.Density SliceDensity slicing visually enhances radar differences based on image brightness. The density-sliced image below has four levels, with higher radar backscatter in the warmer colors.1.From the Display group menu bar associated with the Frost-filtered image, select Tools→Color Mapping→Density Slice. A Density Slice Band Choice dialog appears.2.Select the name of your Frost-filtered image and click OK. A Density Slice dialog appears.3.From the Density Slice dialog menu bar, select File→Restore Ranges. A file selection dialog appears.4.Select dslice.dsr and click Open.5.Click Apply in the Density Slice dialog. Use image-linking and dynamic overlays to compare the density-slicedimage to the gray scale images.6.When you are finished, close the Density Slice dialog.Edge EnhancementA Laplacian filter is a convolution filter that enhances edges in SAR and other data types. A 5 x 5 filter has the following kernel:0 0 -1 0 00 -1 -2 -1 0-1 -2 16 -2 -10 -1 -2 -1 00 0 -1 0 01.From the ENVI main menu bar, select Filter→Convolutions and Morphology.2.From the the Convolutions and Morphology Tool dialog menu bar, select Convolutions→Laplacian.3.Set the Kernel Size field to 5.4.Click Quick Apply. An input band selection dialog appears. Select bonnrsat.img and click OK. Or, view thepre-generated file rsi_f2.dat. Applying the kernel in this manner strongly enhances the edges while losing most of the radiometric information in the image.5.Repeat Steps 1-4, but set the Image Add Back field to 90.6.Click Quick Apply. Or, view the pre-generated file rsi_f3.dat. The following figure shows Laplacian filterresults (left) and 90% Add Back results (right):e image linking and dynamic overlays to compare these results with the original gray scale images.8.When you are finished, close the Convolutions and Morphology Tool dialog.9.From the ENVI main menu bar, select File→Exit.Data FusionSAR data complement other types of data by providing spatial information that other data do not contain. Conversely, SAR data do not have the composition expressed in multispectral, optical data. Therefore, a combined SAR/optical data analysis is usually required.You can use an intensity-hue-saturation (IHS) transform to combine a multispectral, color-composite image with a monochromatic SAR-sharpening band. ENVI provides a simple tool to conduct data merging using IHS.See the Landsat TM and SAR Data Fusion tutorial for more information, and view the pre-generated, fused TM/SAR file rsi_fus.img.Image-Map OutputOutput from ENVI image processing usually consists of a map-oriented, scaled image-map for presentation or visual analysis and interpretation. Radar data can be used in a map composition, just like any other dataset. See the Map Registration and Map Composition tutorials for more information.。
(完整版)03-SAR数据基本处理
– 距离引起的损失（R3）: 接收功率要由传播过程中从远到近的距离 变化校正
辐射定标——结果
• 一般采用以下命名
– Beta Nought (ß°) 雷达亮度（反射率）系数，在斜距方向每单位面 积的反射率单位是无量纲的。这种归一化的优点是不需要入射角 （如散射面积A）
– Sigma Nought (so), 后向散射系数，就是通常说的散射体反射回来 的雷达强度，单位是dB，Sigma nought的定义是假设入射到水平面， 其差异与入射角、波长、极化、散射体的物理性质有关
– Gamma (g) ，用入射角归一化的后向散射系数
辐射归一化
• 严格的定标之后，可识别在距离方向的后向散射系数。因 为地物反射的能量取决于入射角，实际上，获取数据的幅 宽越大，在距离向的后向散射系数的变化越大，这种变化 和散射体的物理特性有关，是不能被校正的，只能通过一 些相对的方法来弥补，如标准化
SAR基本处理技术
技术支持邮箱：ENVI-IDL@ 技术支持热线：400-819-2881-7 /enviidl
主要内容
1. 数据导入 2. 单景雷达影像处理 3. 多时相雷达影像处理
1.数据导入
数据导入
• 目的：生成SARscape识别的数据格式 • 支持SAR数据， 光学数据，高程数据
– 局部入射角校正
– 叠掩/阴影处理
左-后向散射系数，中-局部入射角地图，右-叠掩/阴影地图
练习：地理编码和辐射定标
• 工具：/SARscape/Basic/Intensity Processing/Geocoding/Geocoding and Radiometric Calibration
SAR图像sarscape详细处理过程
SAR图像sarscape详细处理过程来自ENVI-IDL技术殿堂的博客SAR系统可以通过多种方式获得图像，如单通道或双通道模式（如HH、HH / HV或VV / VH)、干涉(单轨或多轨)模式、极化模式(HH,HV,VH,VV)、干涉及极化组合采集模式，不同的获取模式对应了不同的处理方法，可分为以下四种：•雷达强度图像处理•雷达干涉测量（InSAR/DInSAR）•极化雷达处理（PolSAR）•极化雷达干涉测量（PoIInSAR）本文介绍的是雷达强度图像的处理。
1 处理流程如下图是利用SARscape雷达图像基本处理工具，基于不同雷达数据情况，执行雷达图像处理和应用的流程图。
单雷达图像处理与应用流程图单一传感器，单一模式，多时相雷达图像处理与应用流程图单/多传感器，多模式，多时相雷达图像处理与应用流程图2 处理流程关键技术下面介绍流程中相关技术。
(1) 聚焦处理对雷达系统的RAW数据中每个点的反射绿利用经过优化的调焦算法实现数据快速聚焦处理，直接输出单视复数产品数据（SLC数据）。
(2) 多视处理为了得到最高空间分辨率的SAR图像，SAR信号处理器使用完整的合成孔径和所有的信号数据，如单视复数（SLC）SAR图像产品，使得SAR图像包含很多的斑点噪声。
多视处理的目的是为了抑制SAR图像的斑点噪声。
Multilooking工具支持距离向多视和方位向多视，处理得到的多视强度图像是距离向和/或方位向像元分辨率的平均值。
为了提高多视图像的辐射分辨率，降低了空间分辨率。
Multilooking工具支持SLC强度数据或距离向强度数据的输入。
对SLC图像(*_slc)多视处理的结果（右边*_pwr）(3) 图像配准提供Coregistration工具，使用交叉相关技术实现覆盖同一地区的多幅雷达影像的自动配准，以达到亚像素配准精度，整个过程采用全自动的方式。
(4) 滤波Filtering工具提供一系列滤波用于去除雷达图像的斑点噪声，可用于单波段雷达图像和多时相雷达图像。
ENVI对SAR数据的预处理过程(详细版)
一、数据的导入：(1) 在Toolbox 中，选择SARscape ->Basic->Import Data->Standard Formats->ALOS PALSAR。
(2) 在打开的面板中，数据类型（Data Type）：JAXA-FBD Level 1.1。
注：这些信息可以从数据文件名中推导而来。
(3) 单击Leader/Param file，选择d1300816-005-ALPSRP246750820-H1.1__A\LED-ALPSRP246750820-H1.1__A文件。
(4) 点击Data list，选择d1300816-005-ALPSRP246750820-H1.1__A\IMG-HH-ALPSRP246750820-H1.1__A文件(4) 单击Output file，选择输出路径。
注：软件会在输入文件名的基础上增加几个标识字母，如这里增加“_SLC”(5) 单击Start 执行，最后输出结果是ENVI 的slc文件，sml格式的元数据文件，hdr格式的头文件等。
(6) 可在ENVI 中打开导入生成的以slc为后缀的SAR 图像文件。
二、多视单视复数（SLC）SAR 图像产品包含很多的斑点噪声，为了得到最高空间分辨率的SAR图像，SAR 信号处理器使用完整的合成孔径和所有的信号数据。
多视处理是在图像的距离向和方位向上的分辨率做了平均，目的是为了抑制SAR 图像的斑点噪声。
多视的图像提高了辐射分辨率，降低了空间分辨率。
(1) 在Toolbox 中，选择SARscape->Basic ->Multilooking。
(2) 单击Input file 按钮，选择一景SLC 数据（前面导入生成的ALOS PALSAR 数据）。
注意：文件选择框的文件类型默认是*_slc，就是文件名以_slc 结尾的文件，如不是，可选择*.*。
(3) 设置：方位向视数（Azimuth Looks）：5，距离向视数（Range Looks）：1注：详细的计算方法如下所述。
ENVI基本影像处理流程操作
1.快速认识ENVI
•1.1 •1.2 •1.3 •1.4 •1.5 •1.6 ENVI简介 安装目录结构 栅格文件系统和储存 数据输入 数据显示 常见系统设置
1.1ENVI简介——ENVI/IDL体系结构ection 大气校正模 块
ENVI EX 空间特征提 取模块
1.6常见系统设置——显示设置
可以设置三窗口中各个分窗口的显示大小，窗口显示式 样等。其中可以设置数据显示拉伸方式（Display Default Stretch），默认为2%线性拉伸。
基于ENVI的极化SAR数据处理流程介绍
ENVI Tutorial: Polarimetric SAR Processing andAnalysisTable of ContentsO VERVIEW OF T HIS T UTORIAL (2)Background: SIR-C/SAR (2)P REPARE SIR-C D ATA (3)Optional: Read a SIR-C CEOS Data Tape (3)Optional: Multilook SIR-C Data (3)S YNTHESIZE I MAGES (4)Default Polarization Combinations (4)Other Polarization Combinations (4)Display Images (5)Define ROIs for Polarization Signatures (6)Extract Polarization Signatures (6)Adaptive Filters (8)Slant-to-Ground Range Transformation (9)Preview CEOS Header (9)Resample Image (9)Texture Analysis (10)Create Color-coded Texture Map (10)Image-Map Output (11)Overview of This TutorialThis tutorial demonstrates the use of ENVI’s tools for analyzing polarimetric synthetic aperture radar (SAR) data. You will multilook Spaceborne Imaging Radar-C (SIR-C) from Death Valley, California; synthesize images, define ROIs for (and extract) polarization signatures, use adaptive filters, perform slant-to-ground range transformation, use texture analysis, and create an output image-map.Files Used in This TutorialENVI Tutorial Data DVD: envidata\ndv_sircFile Descriptionndv_l.cdp L-band SIR-C subset in ENVI compressed data product (.cdp) formatpol_sig.roi Region of interest (ROI) fileBackground: SIR-C/SARSIR-C is a polarimetric SAR instrument that uses two microwave wavelengths: L-band (24 cm) and C-band (6 cm). The SIR-C radar system was flown as a science experiment on the Space Shuttle Endeavor in April (SRL-1) and October 1994 (SRL-2), collecting high-quality SAR data over many sites around the world. (A second radar system, XSAR, was also flown on this mission, but these data are neither discussed nor processed here.) Additional information about SIR-C is available on the NASA/JPL Imaging Radar Home Page at /.Prepare SIR-C DataThe data used in this tutorial are a subset of L-band Single Look Complex (SLC) SIR-C data that cover the northern part of Death Valley, including Stovepipe Wells, a site of active sand dunes and extensive alluvial fans at the base of mountains. These data were preprocessed by reading and subsetting from tape and multilooking (averaging) to 13 m square pixels. The data are provided in ENVI compressed data product (.cdp) format. This non-image format is similar to the tape format and cannot be viewed until images are synthesized for specific polarizations.The first two functions described in this example—reading the data tape and multilooking—were already applied to the SIR-C data. The steps are provided here only for completeness if you want to learn more about the processes. Skip to the Synthesizing Images section if you are not interested in data preparation.Optional: Read a SIR-C CEOS Data Tape1.From the ENVI main menu bar, select File→Tape Utilities→Read Known Tape Formats→SIR-C CEOS.The SIRC Format - Load Tape dialog appears.2.Enter the tape device name and accept the default record size of 65,536. Click OK. The tape is scanned todetermine what SIR-C files it contains. A dialog appears to let you select the desired datasets. By default, ENVI reads all of the data files on the tape.3.If you do not want to read all of the data files, click Clear, then select the check box next to each desired file.Click OK.4.You can independently subset and multilook the selected data files as they are being read from tape. However,you should perform multilooking on disk (unless you have insufficient disk space) as this function is extremely slow from tape.5.Select a filename, then click Spatial Subset or Multi-Look to enter parameters for the data file. Enter anoutput filename. Each input file must have an output filename. By convention, the output filenames should take the form filename_c.cdp and filename_l.cdp for the C- and L-bands, respectively. The SIR-C data areread from the tape, and one compressed scattering matrix output file is created for selected each dataset.Optional: Multilook SIR-C DataMultilooking is a method for reducing speckle noise in SAR data and for changing the size of a SAR file. You can multilook SIR-C data to a specified number of looks, number of lines and samples, or azimuth and range resolutions. The SIR-C file used in this tutorial was a single-look dataset with a range resolution of 13 m and an azimuth size of 5 m. Multilooking has already been performed in the azimuth direction to make 13 m square pixel sizes. Instructions on multilooking are included here only for completeness.1.From the ENVI main menu bar, select Radar→Polarimetric Tools→Multilook Compressed Data→SIR-C Multilook. An Input Data Product Files dialog appears.2.Click Open File and select an input file. ENVI detects whether the file contains L- or C- band data and displaysthe filename in the appropriate field of the dialog. Click OK.3.Select the file to multilook by selecting the check box next to the name. You can select multiple files.4.Enter any one of three values—number of looks, number of pixels, or pixel size—and the other two are calculatedautomatically. Integer and floating-point number of looks are supported.5.Enter the desired Samples (range) and Lines (azimuth) values.6.Enter a base filename and click OK.Synthesize ImagesThe SIR-C quad-polarization data provided with this tutorial and available on tape from JPL are in a non-image, compressed format. Accordingly, images of the SIR-C data must be mathematically synthesized from the compressed scattering matrix data. You can synthesize images with any transmit and receive polarization combinations you want.1.From the ENVI main menu bar, select Radar→Polarimetric Tools→Synthesize SIR-C Data. An InputProduct Data Files dialog appears.2.Click Open File. A file selection dialog appears.3.Navigate to envidata\ndv_sirc and select ndv_l.cdp. Click Open. When the filename appears in theSelected Files L: field, click OK. The Synthesize Parameters dialog appears.Default Polarization CombinationsFour standard transmit/receive polarization combinations—HH, VV, HV, and TP—are listed in the Select Bands to Synthesize list of the Synthesize Parameters dialog. By default, all of these bands are selected to be synthesized.1.Enter ndv_l.syn in the Enter Output Filename field.2.Click the Output Data Type drop-down list and select Byte. This scales the output data to byte values. (If youwill be performing quantitative analysis, the output should remain in floating-point format.) Click OK. Afterprocessing is complete, four bands corresponding to the four polarization combinations are added to the Available Bands List.Other Polarization CombinationsThe transmit and receive ellipticity and orientation angles determine the polarization of the radar wave used to synthesize an image. The ellipticity angle falls between -45 and 45 degrees and determines the “fatness” of the ellipse. The orientation angle is measured with respect to horizontal and ranges from 0 to 180 degrees. You can synthesize images of non-default polarization combinations by entering the desired parameters as follows.1.From the ENVI main menu bar, select Radar→Polarimetric Tools→Synthesize SIR-C Data. The filendv_l.cdp should still appear in the Selected Files field. Click OK. The Synthesize Parameters dialog appears.2.Enter -45 in both the Transmit Ellip and Receive Ellip fields and 135 in the Transmit Orien and ReceiveOrien fields.3.Click Add Combination. This will produce a right-hand circular polarization image.4.Enter 0 in both the Transmit Ellip and Receive Ellip fields and 30 in the Transmit Orien and Receive Orienfields.5.Click Add Combination. This will produce a linear polarization with an orientation angle of 30 degrees.6.Click Clear under the list of polarization combinations to turn off synthesis of the standard polarization bands,which have already been generated.7.Select the Yes radio button for Output in dB? This will produce images that are in decibels with values typicallybetween –50 and 0.8.In the Enter Output Filename field, enter ndv_l2.syn and click OK. After processing is complete, two bandscorresponding to the polarization combinations are added to the Available Bands List.Display Images1.In the Available Bands List, select [L-TP] under ndv_l.syn and click Load Band. The SIR-C, L-band, total-power image appears in a new display group.2.From the Display group menu bar, select Enhance→Interactive Stretching. A histogram plot windowappears, which shows the current stretch (between the vertical dotted lines on the input histogram) and thecorresponding DN values in the text fields.3.Drag the dotted vertical lines to change the stretch, or enter the desired DN values into the appropriate fields.4.Enter 5 in the left Stretch field and 95 in the right field.5.From the histogram menu bar, select Stretch Type→Gaussian. Click Apply. A Gaussian stretch is appliedwith a 5% low and high cutoff.6.Generate and compare linear and square-root stretches.7.To display a color composite, select the RGB Color radio button in the Available Bands List. Select[L-HH], [L-VV], and [L-HV] in sequential order under ndv_l.syn.8.Click Display #1 and select New Display. Click Load RGB to display the HH band in red, VV in green, and HVin blue. The color variations in the images are caused by variations in the radar reflectivity of the surfaces. The bright areas in the sand dunes are caused by scattering of the radar waves by vegetation (mesquite bushes). The alluvial fans show variations in surface texture due to age and composition of the rock materials.9.Adjust the stretch as desired (Gaussian and square-root stretches work well on all three bands).10.Close the histogram plot window and Display #2 when you are finished. Keep Display #1 open for later exercises.Define ROIs for Polarization SignaturesYou can extract polarization signatures from a SIR-C compressed scattering matrix for a region of interest (ROI) or a single pixel in a polarimetric radar image. Define ROIs by selecting pixels or by drawing lines or polygons within an image.1.From the Display group menu bar, select Overlay→Region of Interest. An ROI Tool dialog appears.2.Four ROIs were previously defined and saved for use in extracting polarization signatures for this tutorial. Fromthe ROI Tool dialog menu bar, select File→Restore ROIs. A file selection dialog appears.3.Select pol_sig.roi. A dialog box appears, stating that the regions were restored. Click OK.4.Regions named veg, fan, sand, and desert pvt appear in the table in the ROI Tool and are drawn in the displaygroup.5.To draw your own ROI, select ROI_Type→Polygon, Polyline, or Point from the ROI Tool menu bar.6.Click New Region, enter a name, and choose a color.Draw polygons by clicking the left mouse button in the display group to select the endpoints of linesegments, or by holding down the left mouse button and moving the cursor for continuous drawing. Clickthe right mouse button once to close the polygon and a second time to accept the polygon.Draw polylines in the same manner as polygons. Click the left mouse button to define the line endpointsand click the right button to end the polyline and a second time to accept the polyline.Point mode is used to select individual pixels. Click the left mouse button to add the pixel currently underthe cursor to the ROI.You can select multiple polygons, lines, and pixels for each ROI.7.Repeat Step 6 to draw a second ROI. You can save the ROIs to a file and restore them later by selecting File →Save ROI from the ROI Tool dialog menu bar.Extract Polarization SignaturesPolarization signatures are 3D representations of the complete radar scattering characteristics of the surface for a pixel or average of pixels. They show the backscatter response at all combinations of transmit and receive polarizations and are represented as either co-polarized or cross-polarized. Co-polarized signatures have the same transmit and receive polarizations. Cross-polarized signatures have orthogonal transmit and receive polarizations. Polarization signatures are extracted from the compressed scattering matrix data using the ROIs for pixel locations. Polarization signatures are displayed in viewer dialogs, as shown on the next page. To extract your own polarization signatures, perform the following steps.1.From the ENVI main menu bar, select Radar→Polarimetric Tools→Extract Polarization Signatures→SIR-C. The filename ndv_l.cdp should appear in the Input Data Product Files dialog. If not, click Open File and select this file. Click OK. The Polsig Parameters dialog appears.2.Select the four ROIs (veg, fan, sand, and desert pvt) by clicking Select All Items.3.Select the Memory radio button and click OK. Four Polarization Signature Viewer dialogs appear, one for eachROI. The polarization signatures are displayed as 3D wire mesh surface plots and as 2D gray scale images. The X and Y axes represent ellipticity and orientation angles, respectively. You can selectively plot the vertical axis as intensity, normalized intensity, or dB by selecting Polsig_Data from the Polarization Signature Viewer dialog menu bar.4.Polarization signature statistics appear at the bottom of each Polarization Signature Viewer dialog. Notice therange of intensity values for the different surfaces. The smoother surfaces (sand and desert pvt) have low Z values. The rough surfaces (fan and veg) have higher Z values. The minimum intensity indicates the pedestal height of the polarization signature. The rougher surfaces have more multiple scattering and therefore higher pedestal heights than the smoother surfaces. The shape of the signature also indicates the scatteringcharacteristics. Signatures with a peak in the middle show a Bragg-type (resonance) scattering mechanism.5.In any given Polarization Signature Viewer dialog, change the Z-axis by selecting Polsig_Data→Normalizedfrom the Polarization Signature Viewer dialog menu bar. This normalizes the signature by dividing by itsmaximum; the signature is plotted between 0 and 1. This representation shows the difference in pedestal heights and shapes better, but it removes the absolute intensity differences.Alternately, select Polsig_Data → Co-Pol and Cross-Pol to toggle between co-polarized and cross-polarized signatures.e the left mouse button to drag a 2D cursor on the polarization signature image on the right side of the plot.Note the corresponding 3D cursor in the polarization plot.7.Click-and-drag any axis to rotate the polarization signature.8.You can optionally output the signatures to a file or printer by selecting File→Save Plot As or File→Printfrom the Polarization Signature Viewer dialog menu bar.9.Close the Polarization Signature Viewer and ROI Tool dialogs when you are finished.Adaptive FiltersAdaptive filters are used to reduce the speckle noise in a radar image while preserving the texture information. Statistics are calculated for each kernel and used as input into the filter, allowing the filter to adapt to different textures within the image.1.From the ENVI main menu bar, select Radar→Adaptive Filters→Gamma. A Gamma Filter Input File dialogappears with a list of open files. You can apply a filter to an entire file or to an individual band.2.In the Gamma Filter Input File dialog, click the Select by toggle button to choose Band.3.Select [L-HH] under ndv_l.syn and click OK. The Gamma Filter Parameters dialog appears.4.Accept the default values, and select the Memory radio button. Click OK.5.In the Available Bands List, click Display #1 and select New Display. Select the Gray Scale radio button,select the new band name (Gamma), and click Load Band.6.From the Display group menu bar, select Enhance→[Image] Square Root.7.In the Available Bands List, click Display #2 and select Display #1. Select [L-HH] under ndv_l.syn, andclick Load Band.8.From the Display #1 menu bar, select Enhance→[Image] Square Root.9.From any Display group menu bar, select Tools→Link→ Link Displays. The Link Displays dialog appears.Click OK to link the gamma-filtered L-HH image (Display #2) with the original L-HH image (Display #1).10.Click in an Image window to toggle between the two images, using the dynamic overlay feature. The figure belowshows a portion of the original image (left) and the gamma-filtered image (right).11.Close Display #2 when you are finished. Leave Display #1 (ndv_l.syn) open for the next exercise.Slant-to-Ground Range TransformationA radar system looks to the side and records the locations of objects using the distance from the sensor to the object along the line of sight, rather than along the surface. An image collected using this geometry is referred to as a slant range image. Slant range radar data have a systematic geometric distortion in the range direction. The true, or ground range, pixel sizes vary across the range direction because of the changing incident angles. This makes the image appear compressed in the near range, relative to what it would look like if all of the pixels covered the same area on the ground. Slant-to-ground range correction for SIR-C is performed on synthesized images. In other words, the correction is not performed on the entire SIR-C compressed data product file. However, this file does store the required information in the CEOS header about the sensor orientation.Preview CEOS Header1.From the ENVI main menu bar, select Radar→Open/Prepare Radar File→View Generic CEOS Header. Afile selection dialog appears. You must select the original unsynthesized data file from which to extract thenecessary information.2.Select ndv_l.cdp and click Open. A CEOS Header Report dialog appears. Scroll down and note that the linespacing (azimuth direction) is 5.2 m, while the pixel spacing (slant range direction) is 13.32 m. Close the CEOS Header Report dialog when you are finished reviewing it.Next, you will use the Slant-to-Ground-Range function to resample the image to square 13.32 m pixels, thusremoving slant range geometric distortion.Resample Image3.From the ENVI main menu bar, select Radar→Slant to Ground Range→SIR-C. A file selection dialogappears.4.Select ndv_l.cdp and click Open. The Slant Range Correction Input File dialog appears.5.Select ndv_l.syn and click OK. The Slant to Ground Range Correction Dialog appears. ENVI automaticallypopulates the Instrument height (km), Near range distance (km), and Slant range pixel size (m) fields withinformation from the CEOS header.6.Enter 13.32 in the Output pixel size (m) field to generate square ground-range pixels.7.From the Resampling Method drop-down list, select Bilinear.8.In the Enter Output Filename field, enter ndv_gr.img. Click OK. The input image is resampled to square13.32 m pixels. Four new bands appear in the Available Bands List. Band 1 of the resampled image correspondsto the L-HH band of the original, slant-range image (ndv_l.syn), Band 2 corresponds to L-VV, etc.9.In the Available Bands List, click Display #1 and select New Display.10.Select a band from the resampled image and click Load Band. The resampled image appears in Display #2.Make sure Display #1 (ndv_l.syn) shows the corresponding polarization band.pare the two images.12.When you are finished comparing images, close Display #2. Keep Display #1 (ndv_l.syn) open for the nextexercise.Texture AnalysisTexture is a measure of the spatial variation in the gray levels in the image, as a function of scale. ENVI calculates texture based on a processing window size you specify. The texture measures demonstrated in this tutorial are Occurrence Measures, including data range, mean, variance, entropy, and skewness. These terms are explained in ENVI Help. Texture is best calculated for radar data with no resampling or filtering applied.1.From the ENVI main menu bar, select Radar→Texture Filters→Occurrence Measures. A Texture InputFile dialog appears.2.Click the Select By toggle button to choose Band. Select [L-HH] under ndv_l.syn and click OK. An OccurrenceTexture Parameters dialog appears.3.Deselect all of the Textures to Compute options except for Data Range.4.Set the Processing Window: Rows and Cols to 7 and 7.5.In the Enter Output Filename field, enter ndv_hh.tex and click OK.Create Color-coded Texture Map6.In the Available Bands List, click Display #1 and select New Display.7.Select Data Range under ndv_hh.tex and click Load Band.8.From the Display #2 menu bar, select Enhance→[Image] Square Root.9.From any Display group menu bar, select Tools→Link→ Link Displays. The Link Displays dialog appears.Click OK to link the original image (Display #1) with the colored texture image (Display #2).10.Click in an Image window to toggle between the two images.11.Double-click inside an Image window to display the Cursor Location/Value tool. Examine the data values in thetextured image, and compare these to the original image.12.From the Display #2 menu bar, select Tools→Color Mapping→Density Slice. A Density Slice Band Choicedialog appears.13.Select the Data Range band and click OK. A Density Slice dialog appears.14.Accept the default ranges by clicking Apply.15.Try creating your own density-sliced image and view the results.16.Keep Display #2 open for the next exercise.Image-Map OutputIn this exercise, you will create a map of your color-coded textured image and add a border and map key.1.From the Display #2 menu bar, select Overlay→Annotation. An Annotation dialog appears.2.From the Annotation dialog menu bar, select Options→Set Display Borders.3.In the Display Borders dialog, enter 100 in the upper field, and leave the remaining fields 0.4.Click Border Color and select Items 1:20→White. Click OK. This adds a 100-pixel white border at the top ofthe image.5.Move the Image box in the Scroll window to the top of the image containing the border.6.Enter a map title in the empty field in the Annotation dialog. Set the Size value to 16. Click the Color box onceto select black.7.Click in the Image window to show the map title, then move it inside the white border to the far left. Right-clickto lock the map title in place. You can place multiple text items on the image in this manner, and you can change their font size, type, color, and thickness as desired.8.From the Annotation dialog menu bar, select Object→Color Ramp.9.Enter Min and Max values of 0 and 255 respectively, set Inc to 4, and set the font Size to 14 to annotate thecolor ramp.10.Click in the Image window to show the map key, move it inside the white border to the far right, then right-clickto lock it in place. The following figure shows a sample map; your results may be different.11.Save the image to a PostScript file by selecting File→Save Image As→Postscript File from the Display #2menu bar. An Output Display to PostScript File dialog appears.12.Leave the default values, and enter an output filename or accept the default name of ndv_hh.ps. Click OK.Or, output the map directly to your printer by selecting File → Print from the Display #2 menu bar.13.When you are finished, select File→Exit from the ENVI main menu bar.。
卫星遥感技术的数据处理与解译教程
卫星遥感技术的数据处理与解译教程卫星遥感技术是一种通过卫星传感器获取地球表面信息的技术手段。
随着遥感卫星的发展和技术的进步，遥感数据的获取和处理已成为地学研究和资源管理中不可或缺的工具。
在这篇文章中，我们将向您介绍卫星遥感技术的数据处理与解译方法，帮助您快速掌握基本操作和技巧。
一、遥感数据处理的步骤1. 数据获取与选择首先，我们需要获取适合研究的遥感数据。
常见的卫星遥感数据包括Landsat、Sentinel、MODIS等系列数据。
根据具体研究需求，可以选择不同波段、分辨率和时间段的数据。
2. 数据预处理在使用遥感数据进行研究之前，我们需要对原始数据进行预处理。
这包括大气校正、辐射校正和几何校正等步骤，以确保数据的准确性和可比性。
3. 影像增强为了提取地物信息和进行可视化分析，我们可以对遥感影像进行增强处理。
常见的增强方法包括直方图均衡化、滤波和波段合成等。
4. 分类与分类精度评价遥感数据的分类是指将影像中的像素分配到不同的地物类别中。
常见的分类方法包括监督分类和无监督分类。
分类的结果需要进行分类精度评价，以验证分类准确性和可信度。
5. 特定应用的数据解译根据具体的应用需求，我们可以通过遥感数据解译获取所需的地物信息。
例如，利用NDVI（归一化植被指数）可以提取植被分布信息，利用NDWI（归一化水体指数）可以提取水体分布信息。
6. 数据分析与建模在获取地物信息之后，我们可以进行数据分析和建模，以深入研究地球表面的动态变化和环境响应。
常见的分析方法包括变化检测、时间序列分析和空间模型构建等。
二、常用的遥感数据处理软件1. ENVI（Environment for Visualizing Images）ENVI是一款功能强大的遥感数据处理软件，具有丰富的图像增强、数据分类和解译功能。
通过ENVI，用户可以方便地进行遥感数据的处理和分析。
2. ArcGIS（Arc Geographic Information System）ArcGIS是一款广泛使用的地理信息系统软件，同样提供了丰富的遥感数据处理和空间分析功能。
基于ENVI的TM与SAR数据融合处理流程介绍
ENVI Tutorial:Landsat TM and SAR DataFusionTable of ContentsO VERVIEW OF T HIS T UTORIAL (2)D ATA F USION (3)Preparing Images (3)R OME,I TALY,D ATA F USION E XAMPLE (4)Read and Display Images (4)Register the TM image to the ERS-2 image (4)Perform HSI Transform to Fuse Data (5)Display and Compare Results (5)Overview of This TutorialThis tutorial is designed to demonstrate selected ENVI data fusion capabilities. You will co-register Landsat Thematic Mapper (TM) data and ERS-2 synthetic aperture radar (SAR) data of Rome, Italy using image-to-image registration. You will fuse the two datasets using a hue-saturation-intensity (HSI) color transform, and you will compare the fused data to the individual datasets.ERS-2 and Landsat images used in this tutorial are provided courtesy of the European Space Agency (ESA) and Eurimage (used with permission) and may not be redistributed without explicit permission from these organizations.For additional data fusion details, please see ENVI Help.Files Used in This TutorialENVI Resource DVD: envidata/rometm_ersFile Descriptionrome_tm (.hdr) Landsat TM data of Rome, Italyrome_ers2 (.hdr) ERS-2 SAR data of Rome Italyromr_tm.pts Ground control points (GCPs) for image-to-imageregistrationData FusionData fusion is the process of combining multiple image layers into a single composite image. It is commonly used to enhance the spatial resolution of multispectral datasets using high spatial resolution panchromatic or single-band SAR data.The following sections demonstrate the preparation required to fuse image datasets in ENVI, and how to perform data fusion.Preparing ImagesTo perform data fusion in ENVI, the files must either be georeferenced (in which case spatial resampling is performed on the fly), or, if not georeferenced, cover the same geographic area, have the same pixel size, have the same image size, and have the same orientation. The files used in this exercise are not georeferenced. Therefore, the low spatial resolution images must be resampled to have the same pixel size as the high spatial resolution image (using nearest-neighbor resampling).Rome, Italy, Data Fusion ExampleRead and Display Images1.From the ENVI main menu bar, select File→ Open Image File. Navigate to envidata\rometm_ers andselect rome_ers2. Click Open. This file contains ERS-2 SAR data.2.In the Available Bands List, select the Gray Scale radio button. Select Band 1 under rome_ers2 and click LoadBand.3.From the ENVI main menu bar, select File→Open Image File. Select rome_tm. Click Open. This file containsLandsat TM data.4.In the Available Bands List, click Display #1 and select New Display.5.Select the RGB Color radio button. Select Band 4, Band 3, and Band 2 in sequential order. Click Load RGB todisplay rome_tm as a false-color composite into Display #2.Following is a comparison of the Landsat TM false-color composite (left) and the ERS-2 SAR gray scale image(right):Register the TM image to the ERS-2 image1.From the ENVI main menu bar, select Map →Registration→Select GCPs: Image-to-Image. An Image toImage Registration dialog appears.2.Under Base Image, select Display #1 (ERS-2 data). Under Warp Image, select Display #2 (TM data). ClickOK. A Ground Control Points Selection dialog appears.3.From the Ground Control Points Selection dialog menu bar, select File→Restore GCPs from ASCII. A fileselection dialog appears.4.Select rome_tm.pts and click Open.5.Pre-selected GCPs are loaded into both the TM and ERS-2 display groups. Review the positions of these points inboth images for accuracy, and observe the total RMS error listed at the bottom of the Ground Control PointsSelection dialog.6.Click Show List. In the Image to Image GCP List that appears, scroll to the right and review the RMS values foreach GCP. These GCPs are sufficient for a quick registration and for this exercise; however, adding more GCPs will improve the match between images. See the tutorial Image Georeferencing and Registration for additional details about performing image-to-image registration. From the Ground Control Points Selection dialog menu bar, select File→Cancel.7.From the Ground Control Points Selection dialog menu bar, select Options→Warp File. A file selection dialogappears. Select rome_tm and click OK to warp all seven TM bands to match the ERS-2 data. A RegistrationParameters dialog appears.8.Enter the following values for Output Image Extent:Upper Left X: 1Upper Left Y: 1Output Samples: 5134Output Lines: 55499.Accept the default values for the remaining fields. In the Enter Output Filename field, enter register_tm.Click OK to perform the image-to-image registration.10.In the Available Bands List, click Display #2 and select New Display.11.In the Available Bands List, select the RGB Color radio button. Select Warp bands 4, 3, and 2 underregister_tm and click Load RGB to display the registered TM image as a false-color composite in Display #3.Perform HSI Transform to Fuse Data1.From the ENVI main menu bar, select Transform→Image Sharpening→HSV. A Select Input RGB dialogappears.2.Select Display #3 (which contains register_tm) and click OK. A High Resolution Input File appears.3.Select Band 1 under rome_ers2 and click OK. An HSV Sharpening Parameters dialog appears.4.In the Enter Output Filename field, enter rome_fused.img and click OK.Display and Compare Results1.In the Available Bands List, select the RGB Color radio button. Click Display #2.2.Select the HSV Sharp R, G, and B bands under rome_fused.img in sequential order. Click Load RGB to loadthe HSV-sharpened, fused, color image into Display #2, replacing the original TM image.Following is a subset of the fused image:3.From a Display group menu bar, select Tools→Link→Link Displays. A Link Displays dialog appears.4.Click OK to link Display #1 (original ERS-2 image), Display #2 (fused image), and Display #3 (registered TMimage). Compare these three images.5.Try fusing other color composites with the ERS-2 data as above and compare the results.6.When you are finished, exit ENVI.。
02_SAR数据基本处理
没限制； • 短基线（SBAS）渐进式处理，提高处理效率。
SARscape国内外主要用户
支持多源雷达系统
– ERS-1/2 – JERS-1 – RADARSAT-1 – RADARSAT-2 – ENVISAT ASAR – ALOS PALSAR – TerraSAR-X – TanDEM-X(+bistatic) – COSMO-SkyMed – OrbiSAR-1 (X、P-band) – E-SAR – RISAT-1 – STANAG 7023 – RAMSES – TELAER – GLAS/IceSat DEM
JSC KrasnoyarskGeofizika, Russia KEOC, Korea KKC - Kokusai Kogyo Co.,Ltd, Japan Kongsberg Satellite Services AS,
Norway LLC VNIIGAZ, Russia Lockheed Martin, USA MDA, USA & Canada OHB System GmbH, Germany PASCO Cooperation, Japan Peking University, China Pöyry Environment Oy, Finland Raytheon, USA Russian Academy of Sciences, Russia SaTReCi, Korea Statoil Hydro, Norway SWF Studio, Italy TELESPAZIO, Italy TOTAL, France USGS, USA ……
最新版ENVI53下高分二号(GF2)数据预处理资料
ENVI5.3下高分二号（GF2）数据预处理以一景2015年1月23日获取的GF2-PMS1数据为例介绍在ENVI5.3下GF2数据预处理的详细操作步骤。
GF2数据预处理基本流程如下：图：GF2数据预处理流程说明：1. 针对不同的应用，有不同的处理流程，上图中列出了两种常用的预处理流程。
流程一主要针对高精度的定量遥感应用，也就是对大气校正精度要求比较高应用，比如：植被参数定量反演等；流程二主要针对定性遥感或者对大气校正精度要求比较低的遥感应用，比如：土地利用类型分类等。
本文介绍的主要是流程二的详细操作步骤，流程一的实现可参考日志：ENVI5.2下高分二号数据FLAASH大气校正；另外，中国资源卫星应用中心网站已经公布了最新的GF2数据绝对辐射定标系数和两个传感器的波谱响应函数，大家可以下载使用。
2. 本例中所有操作都是在ENVI5.3版本下进行的，除NNDiffuse Pan Sharpening图像融合（ENVI5.2新增，ENVI5.1中可以使用G-S融合方法）外，其他操作在ENVI5.1/5.2下同样可以完成。
1. 数据打开启动ENVI5.3，在菜单栏中，选择File > Open，弹出Open对话框，找到GF2数据文件夹所在位置，选中扩展名为.tiff的两个文件，点击打开。
图2 打开GF2多光谱和全色数据在左侧图层管理Layer Manager面板中，选择多光谱或全色数据图层，右键View Metadata查看其元数据信息，可以看到ENVI很好地识别了数据的RPC信息。
图3 ENVI自动识别GF2数据RPC信息2. 正射校正有了RPC信息之后，下面我们就可以基于这些RPC信息分别对多光谱和全色数据进行正射校正。
这里我们以多光谱数据正射校正为例，全色数据正射校正操作完全相同。
在Toolbox中，选择Geometric Correction > Orthorectification > RPC Orthorectification Workflow，打开正射校正流程化工具。
ENVI高光谱数据处理流程
高级设置
• 光谱定义文件：内置AVIRIS、HYMAP、HYDICE、HYPERION、 CASI、AISA。
• 气溶胶厚度系数：用于技术邻域效应范围。一般值为1~2km。 • CO2混合比率：默认为390ppm。 • 使用领域纠正。 • 使用以前的MODTRAN模型计算结果。 • 设置MODTRAN模型的光谱分辨率（推荐值5 cm-1)。 • 设置MODTRAN多散射模型。 • 提供三种模型供选择Isaacs，DISORT和 Scaled DISORT。默认是
和几个植被波谱
– Johns hopkins university 0.4~14um 矿物波谱
– IGCP264 (项目) 到
26个质优样品应用波谱仪测量得
• 打开波谱库（spectral/spectral libraries/…view)
• 显示波谱曲线(点击）
• 创建波谱库（spectral/spectral libraries/…builder)
• 两种气溶胶去除方法
– 2-Band（K-T）方法(类似模糊减少法)，如果没有找到适应的黑 值（一般是阴影区或者水体），系统将采用能见度值来计算；所 以即使选择了该选项也要给能见度。
– 选择None，采用能见度值参与气溶胶去除，能见度值大约参考值 参见表
天气条件 晴朗 中等雾、阴霾 厚雾、阴霾
能见度
3、物质制图与识别、探测
•波谱库 •波谱分析 •端元波谱收集 •高光谱制图与识别
波谱库
• ENVI波谱库 （安装目录下spec_lib）
– Jet Propulsion Laboratory 0.4~2.5um 160种纯矿物波谱
解决ENVI SARscape里面SAR数据地理编码添加DEM数据的问题
解决ENVI SARscape里面SAR数据地理编码添加DEM数据的问题
在envisarscape中，进行地理编码时，分为椭球体编码和地形编码，其中地形编码需要加载DEM数据。
但是其默认格式为_dem。
对于刚接触ENVISAESCAPE的来说，有些某不着头脑。
我也是耗费了好几天才解决这个问题。
现在把过程分享给大家。
其实很easy!
首先，展示一下问题界面
点击SARscape>Import Data>Generic Format>Tiff，将DEM文件加载进来，若不是TIF格式，请首先转换成TIF格式。
这个步骤很简单，想必大家都会。
如下界面
And then
这时候，再在SARscape>Basic>Intensity>Geocoding>Geocoding and Radiometric Calibration中，进行地理编码。
这个时候的文件夹里就能看到_dem的文件了。
其实很简单，就是要把DEM转成SARscape可以接受的格式。
ENVI基本影像处理流程操作
1.1ENVI简介—NITF—数据支持扩展模块 ——NITF
•读写、显示标准NITF格式文件 •JPEG2000编码压缩NITF格式文件 •支持NITF2.0、NITF2.1和NSIF1.0之间的转换 •读写从商业卫星、NCDRD和 第二图像格式（ NSIF）中获得
政府标准数据 •广泛支持NSDE的分类或未分类的TREs，也 包括自定义的
1.快速认识 ENVI
•1.1 ENVI简介 •1.2 安装目录结构 •1.3 栅格文件系统和储存 •1.4 数据输入 •1.5 数据显示 •1.6 常见系统设置
1.1ENVI简介——ENVI/IDL体系结构
扩展模块 主模块 开发语言
Atmospheric Correction
大气校正模 块
Feature Extraction
ENVI基本影像处理流程操 作
邓书斌 E-mail:dengsb@ ESRI中国（北京）有限公司 遥感事业部
主要内容
• 1.快速认识ENVI • 2.影像一般预处理 • 3.影像信息基本提取方法 • 4.制图与三维可视化
遥感图像处ห้องสมุดไป่ตู้的一般流程
数据的输入输出 图像显示与分析 图像预处理（几何纠正、融合、镶嵌等） 影像信息提取（人工解译、自动分类、特征提取、动态检测等） 专题制图/三维可视化分析（集成GIS现有数据) 成果报告（GIS分析/共享）
SRF
GeoTIFF HDF
MAS-50 MRLC (.dda)
TIFF
1.5 数据输入——特定数据的打开
• 对于特定的已知文件类型，利用内部或外部 的头文件信息 通常会更加方便。使用 Open External File 选项，ENVI 能够读取一些标准 文件类型的若干格式，包括精选的遥感 格式、 军事格式、数字高程模型格式、图像处理软 件格式 及通用图像格式。ENVI 从内部头文 件读取必要的参数，因 此不必在Header Information对话框中输入任何信息。
envi影像解译的步骤
envi影像解译的步骤一、影像预处理在进行影像解译之前，首先需要进行影像预处理，以提高解译的准确性和可靠性。
影像预处理主要包括辐射校正、大气校正、几何校正等步骤。
1. 辐射校正辐射校正是将影像中的数字值转换为与辐射度量相关的物理量，如辐射亮度。
这一步骤可以通过查阅影像的元数据获取相关参数，并根据这些参数进行校正计算。
2. 大气校正大气校正是为了消除大气对影像亮度的影响，以得到地物反射率。
一般可以利用大气传输模型来估算大气对地物的衰减程度，并根据估算结果进行校正。
3. 几何校正几何校正是为了消除影像中的几何畸变，使得影像与实际地面一一对应。
这一步骤可以利用地面控制点进行校正，通过对控制点的坐标测量和影像中对应点的像素坐标计算出几何变换参数，然后对整个影像进行校正。
二、特征提取在影像预处理之后，可以进行特征提取，以获得影像中感兴趣的地物信息。
特征提取主要包括目标检测、分类和边界提取等步骤。
1. 目标检测目标检测是为了在影像中自动识别出感兴趣的目标，如建筑物、道路、植被等。
常用的目标检测方法包括基于像素的阈值分割、基于纹理和形状特征的分类等。
2. 分类分类是为了将影像中的像素或区域划分到不同的类别中，以获得地物类型信息。
分类可以基于统计学方法、机器学习方法或深度学习方法进行，常用的分类算法有最大似然分类、支持向量机、随机森林等。
3. 边界提取边界提取是为了获得地物的边界信息，以便进行进一步的分析和应用。
常用的边界提取方法包括边缘检测算法、形态学运算等。
三、数据分析与解译在完成特征提取之后，可以进行数据分析和解译，以获取更深层次的地物信息和地物属性。
1. 数据分析数据分析主要是对提取的特征进行统计和分析，以获取地物的数量、分布、特征等信息。
常用的数据分析方法包括统计学分析、空间分析等。
2. 解译解译是为了从影像中获取地物属性信息，如植被覆盖度、土地利用类型等。
解译可以基于先验知识、地面调查数据或其他辅助数据进行。
ENVI对SAR数据的预处理过程详细版
ENVI对SAR数据的预处理过程详细版合成孔径雷达（Synthetic Aperture Radar，简称SAR）是一种主动遥感技术，通过发射雷达脉冲，并接收其回波来获取地面的图像信息。
ENVI是一种常用的遥感数据处理软件，可以用来对SAR数据进行预处理、图像增强和地物提取等操作。
下面将详细介绍ENVI对SAR数据的预处理过程。
SAR数据预处理主要包括数据读取、辐射定标、多视同相运算、滤波和几何校正等步骤。
首先，打开ENVI软件，加载SAR数据。
在ENVI中，可以选择File -> Open菜单，然后选择SAR数据文件进行载入。
接下来，进行辐射定标，将原始SAR数据转换为幅度或强度图像。
SAR数据的辐射定标是将回波强度转换为真实物理值的过程。
辐射定标的目的是根据接收到的雷达回波信号的强度，恢复出地物散射系数的物理量级。
ENVI提供了多种辐射定标方法，可以根据数据的特点选择合适的方法进行定标。
接下来，进行多视同相运算。
SAR数据在地形起伏影响下会出现像素间的相位差，导致地物在图像中出现模糊。
多视同相运算是通过多个视角拍摄的SAR图像之间的相位差来消除地形影响，获得更清晰的图像。
ENVI提供了多视同相运算的功能，在菜单中选择SAR -> SAR Geocode ->Multi-look进行设置和运算。
然后，进行滤波处理。
滤波是为了去除图像中的噪声和干扰，提高图像质量。
常用的滤波方法有均值滤波、中值滤波、高斯滤波等。
ENVI提供了多种滤波方法，在菜单中选择SAR -> Speckle Reduction可以对SAR图像进行滤波处理。
最后，进行几何校正。
几何校正是将SAR图像与地理坐标系统对齐，使其与其他地理信息数据进行叠加和分析。
ENVI提供了几何校正的功能，在菜单中选择SAR -> SAR Geocode -> Geocoding进行设置和处理。
在进行SAR数据预处理时，还需要注意一些事项。
应用ENVI软件进行SPOT卫星数据处理过程
应用ENVI软件进行SPOT卫星数据处理过程ENVI（Environment for Visualizing Images）软件是一种用于处理和分析遥感数据的强大工具。
它提供了一系列功能强大的图像处理工具，可以在处理SPOT卫星数据时提供全面的支持。
下面是一个关于如何使用ENVI软件进行SPOT卫星数据处理过程的1200字以上的介绍。
第二步是导入SPOT卫星数据。
SPOT卫星数据通常以一系列的影像文件的形式存在，这些文件包含了卫星传感器采集的遥感影像。
在ENVI软件中，可以使用“File”->“Open”命令导入这些影像文件。
ENVI可以自动识别和加载这些文件，创建一个多波段的影像数据栈。
第三步是显示和探索SPOT卫星数据。
ENVI提供了丰富的显示选项，可以对SPOT卫星数据进行可视化。
在ENVI中，可以使用工具栏上的显示按钮来选择不同的显示模式，例如真彩色、假彩色和单波段显示。
也可以使用工具栏上的缩放和漫游按钮来放大、缩小和平移图像。
第四步是进行预处理。
预处理是为了清洁和增强影像数据。
在ENVI 中，可以使用一系列的预处理工具来去除噪声、校正辐射和大气等。
例如，可以使用大气校正工具来校正影像中的大气干扰，使得影像更准确地反映地面特征。
第五步是进行图像分类和分类评估。
根据遥感数据具有不同的反射率特征，可以使用ENVI中的分类工具将影像像素分成不同的类别。
这有助于分析图像中的地表特征和目标。
ENVI提供了一些常见的分类算法，如最大似然分类、支持向量机和随机森林。
第六步是进行图像处理和分析。
一旦完成了分类，可以使用ENVI中的图像处理和分析工具对数据进行进一步处理。
例如，可以计算图像中不同类别的面积、周长和形状等指标。
也可以进行基于对象的图像分析，提取一些地物的特征。
第七步是生成和输出结果。
在ENVI中，可以使用工具栏上的输出按钮将处理和分析的结果生成为图像、表格或报告等形式。
可以选择不同的输出格式和存储路径。
ENVI实验步骤
ENVI实验步骤ENVI是一种功能强大的遥感图像处理和分析软件，可以用于获取、处理和分析遥感数据。
以下是使用ENVI进行图像处理和分析的一般步骤。
1.导入图像首先，打开ENVI软件并导入需要处理和分析的遥感图像。
可以通过选择文件>打开或使用ENVI自带的导航栏来导入图像。
2.图像预处理在导入图像后，进行图像预处理以消除噪声、增强图像质量等。
常见的图像预处理步骤包括图像大气校正、噪声过滤、辐射定标等。
3.图像增强根据需要，对图像进行增强以使特定信息更加明显。
图像增强技术包括直方图均衡化、滤波器应用、对比度增强等。
这些技术可使目标在图像中更易于识别和分析。
4.特征提取根据您的研究需求，从图像中提取相应的特征。
特征提取可以包括目标识别、目标跟踪、边缘检测等。
ENVI提供了多种特征提取工具和算法，可根据研究要求选择适当的工具。
5.分类与分类精度评估根据您的研究目的，使用ENVI进行图像分类。
图像分类是将图像中的像素聚类到不同的类别中，以识别不同的地物类型。
ENVI提供了各种分类方法，包括监督和非监督分类。
分类完成后，可以使用分类精度评估工具对分类结果进行准确性评估。
6.可视化和解释结果对于处理和分析后的图像结果，可以使用ENVI的可视化工具对其进行可视化。
ENVI提供了丰富的图像展示和解释工具，如虚拟视窗、伪彩色图像、饼图、散点图等。
这些工具可以帮助您更好地理解和解释图像结果。
7.数据输出和报告生成最后，根据需要将处理和分析后的结果导出到不同的格式，如栅格图像、矢量文件、表格等。
ENVI还提供了生成报告的功能，将结果放入报告中以便进一步展示和共享。
总结而言，使用ENVI进行图像处理和分析的一般步骤包括图像导入、图像预处理、图像增强、特征提取、分类与分类精度评估、结果可视化和解释以及数据输出和报告生成。
这些步骤可根据研究目的和需求进行适当调整和改变。
ENVI Sarscape入门教程V1.1
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一、数据的导入：(1) 在Toolbox 中，选择SARscape ->Basic->Import Data->Standard Formats->ALOS PALSAR。
(2) 在打开的面板中，数据类型（Data Type）：JAXA-FBD Level 1.1。
注：这些信息可以从数据文件名中推导而来。
(3) 单击Leader/Param file，选择d1300816-005-ALPSRP246750820-H1.1__A\LED-ALPSRP246750820-H1.1__A文件。
(4) 点击Data list，选择d1300816-005-ALPSRP246750820-H1.1__A\IMG-HH-ALPSRP246750820-H1.1__A文件(4) 单击Output file，选择输出路径。
注：软件会在输入文件名的基础上增加几个标识字母，如这里增加“_SLC”(5) 单击Start 执行，最后输出结果是ENVI 的slc文件，sml格式的元数据文件，hdr格式的头文件等。
(6) 可在ENVI 中打开导入生成的以slc为后缀的SAR 图像文件。
二、多视单视复数（SLC）SAR 图像产品包含很多的斑点噪声，为了得到最高空间分辨率的SAR图像，SAR 信号处理器使用完整的合成孔径和所有的信号数据。
多视处理是在图像的距离向和方位向上的分辨率做了平均，目的是为了抑制SAR 图像的斑点噪声。
多视的图像提高了辐射分辨率，降低了空间分辨率。
(1) 在Toolbox 中，选择SARscape->Basic ->Multilooking。
(2) 单击Input file 按钮，选择一景SLC 数据（前面导入生成的ALOS PALSAR 数据）。
注意：文件选择框的文件类型默认是*_slc，就是文件名以_slc 结尾的文件，如不是，可选择*.*。
(3) 设置：方位向视数（Azimuth Looks）：5，距离向视数（Range Looks）：1注：详细的计算方法如下所述。
另外，单击Look 按钮可以估算视数。
(4) Border Resize 选项，选择此项，会对检测结果边缘中的无效值，进而重新计算输出图像的大小。
这里不选择。
(5) 输出路径会依据软件默认参数设置自动添加或自行修改，单击Start 按钮执行。
(6) 计算完之后在Display 中显示结果，可以看到图像的斑点噪声得到的抑制，但是降低了空间分辨率注：方位向视数(Azimuth Looks)；距离向视数(Range Looks)视数跟距离向分辨率、方位向分辨率以及中心入射角有关。
精确的计算方法如下：地面分辨率= pixel spacing slant range / sin(incidence angle)•pixel spacing azimuth = 3.169m （方向分辨率）•pixel spacing slant range = 9.368m （距离分辨率）•incidence angle scene centre = 34.3°•地距的距离分辨率：9.368 / sin(34.3°) = 16.62 m，距离向视数为1•地距的方位向分辨率经过多视后保持与地距的距离分辨率一致•16.62/3.169=5，因此方位向视数为5三、滤波从连贯SAR 传感器中获取的图像都有斑点噪声，SARscape 提供两大类滤波，用于单波段雷达图像的滤波和多时相雷达图像滤波。
(1) Toolbox 中，选择SARscape->Basic->Filtering->Single Image。
(2) 打开Filtering Single Image 面板，单击Input file 按钮，选择前面得到的多视处理结果。
(3) 有8 中滤波供选择，选Frost，同时会打开Frost Lee/Refined Lee 对话框。
(4) 在Frost Lee/Refined Lee 对话框中，分别设置Azimuth Window Size 和Range WindowSize：9(5) 单击Start 执行。
四、地理编码和辐射定标SAR 系统是测量发射和返回脉冲的功率比，这个比值（就是后向散射）被投影为斜距几何。
由于不同SAR 传感器或者不同接收模式，为了更好的对比SAR 图像几何和辐射特征，需要将SAR 数据从斜距或地距投影转换为地理坐标投影（制图参考系）。
(1) Toolbox 中，选择SARscape->Basic->Geocoding->Geocoding and Radiometric Calibration。
(2) 在Geocoding and Radiometric Calibration 面板中，单击Input file 按钮，选择前面Frost 滤波做的结果。
(3) Cartographic System，设置输出投影参数。
(5) 像元大小（GRID SIZE）：x：25，y：25。
(6) 重采样方法（RESAMPLE）：有5 中方法供选择，从左到右精度提高，但是速度越慢。
这里选择Optimal Resolution。
(7) 选择辐射定标（Radiometric Calibration）(8) 其他可选项：辐射归一化（Radometric），局部入射角校正（Local Incidence Angle）、叠掩/阴影处理（Layover/Shadow）(9) 单击Start 执行。
五、镶嵌工具在Toolbox中启动/Mosaicking/Seamless Mosaic。
1）点击左上的加号（如图）添加需要镶嵌的影像数据。
（2）添加进来之后可以看到数据的位置和重叠关系和影像轮廓线。
（3）勾选Show Preview，可以预览镶嵌效果。
在Data Ignore Value一览输入透明值，这里输入0。
如下图为0值透明的效果。
匀色处理：提供的匀色方法是直方图匹配。
（1）在Corlor Correction选项中，勾选Histogram Matching：•∙Overlap Area Only：重叠区直方图匹配•∙Entire Scene：整景影像直方图匹配（2）在Main选项中，Color Matching Action中右键设置参考（Reference）和校正（Adjust），根据预览效果确定参考图像。
接边线与羽化接边线包括自动和手动绘制两种方法，也可以结合起来使用。
（1）选择下拉菜单Seamlines->Auto Generate Seamlines，自动绘制接边线，如下图所示，自动裁剪掉ＴＭ边缘“锯齿”。
（2）自动生成的接边线比较规整，可以明显看到由于颜色不同而显露的接边线。
下拉菜单Seamlines-> Start editing seamlines，编辑接边线，可以在接边处绘制多边形，之后自动将绘制的多边形作为新的接边线。
输出结果切换到Ｅｘｐｏｒｔ选项，这是输出文件名、路径、格式、波段、背景值、重采样方法等信息。
六、融合不同传感器图像融合下面以SPOT4 的10米全色波段和Landsat5 TM 30m多光谱的融合操作为例，学习图像融合操作流程，数据存放在"…\08.图像融合\数据\TM与spot"中。
（1）选择File > Open，将SPOT4数据bldr_sp.img和Landsat TM数据TM-30m.img 分别打开。
（2）在Toolbox中，打开 / Image Sharpening /Gram-Schmidt Pan Sharpening，在文件选择框中分别选择bldr_sp.img作为低分辨率影像（Low Spatial）和bldr_sp.img作为高分辨率影像（High Spatial），单击OK。
打开Pan Sharpening Parameters面板。
（3）在Pan Sharpening Parameters面板中，选择传感器类型（Sensor）：Unknown，重采样方法（Resampling）：Cubic Convolution，输出格式为：ENVI。
（4）选择输出路径及文件名，单击OK执行融合处理。
注：进度条显示在右下角。
相同传感器图像融合对于高分辨率影像，同样可以Gram-Schmidt Pansharping融合方法达到更好的效果，下面以QuickBird影像为例介绍这种融合方法，数据存放在"…\08.图像融合\数据\ Quikbird"中。
ENVI5.1中，pansharping融合方法就是Gram-Schmidt PanSharpening选项。
（1）File > Open，打开影像文件qb_boulder_msi.img和qb_boulder_pan.img。
（2）在Toolbox中，打开 / Image Sharpening /Gram-Schmidt Pan Sharpening，在文件选择框中分别选择qb_boulder_msi.img作为低分辨率影像（Low Spatial）和qb_boulder_pan.img作为高分辨率影像（High Spatial），单击OK。
打开PanSharpening Parameters面板。
（3）在Pan Sharpening Parameters面板中，选择传感器类型（Sensor）：QuickBird，重采样方法（Resampling）：Cubic Convolution，输出格式为：ENVI。
注：传感器类型（Sensor）中还包括：GeoEye-1、Goktruk-2、IKONOS、landsat8_oli、landsat8_tirs、NPP VIIRS、Pleiades-1A/B、QuickBird、UI:GSS：Sensorrasat、Spot-6、Landsat ETM、WordlView-1/2。
（4）选择输出路径及文件名，单击OK执行融合处理。
注：进度条显示在右下角。
七、裁剪规则图像裁剪规则裁剪，是指裁剪图像的边界范围是一个矩形，这个矩形范围获取途径包括：行列号、左上角和右下角两点坐标、图像文件、ROI/矢量文件。
规则分幅裁剪功能在很多的处理处理过程中都可以启动（Spatial Subset）。
下面介绍其中一种规则分幅裁剪过程。
以TM影像为例，图像存放在"…\10.图像裁剪\数据"中。
（1）File > Open打开图像Beijing_TM.dat，按Linear2%拉伸显示。
（2）File > Save As，进入File Selection面板，选择Spatial Subset选项，打开右侧裁剪区域选择功能。