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supervised and unsupervised classification in erdas imagine

supervised and unsupervised classification in erdas imagine

You can use these class numbers to aggregate your classes using the Grouping Tool that is available from the Supervised classification in Classification group. After opening Grouping Tool, load the image you just created using the unsupervised classification. In Close the Formula window. Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. ERDAS IMAGINE uses the ISODATA algorithm to perform an unsupervised classification. Mather, P. (1999) Computer Processing of Remotely-Sensed Images, An Itroduction, 2nd ed. Abstract: ERDAS IMAGINE is a remote sensing image processing system developed by the United States.The paper using ERDAS to classified the remote sensing of Land-sat TM image data by supervised classification method and unsupervised classification method, Using the Yushu City remote sensing image of Jilin Province as the trial data, and classified the forest, arable land and water from the … 5- unsupervised classification in Erdas Imagine 8.5. The spectral pattern present within the data for each pixel was used as the numerical basis for categorization. The first stage of the supervised classification process is to collect reference training sites for each land cover type in order to generate training signatures. The Recode dialog opens, select the Input file and also the Output file. classification, the red, green and blue comp osite of bands 4, 3 and 2 was used. 2D View – Displays raster, vector, and annotation data in a 2-dimensional view window; 3D View – Renders 3-dimensional DEMs, raster overlays, and vector and annotation feature layers; Map View – A View window designed for creating cartographic-quality maps and presentation graphics, usual output to a hardcopy device. Model outputs incorrect/ invalid. A Maximum percentage of unchanged pixels has reached between two iterations. Choose the Classifier button to access the menu, and Unsupervised Classification… to enter the setup dialog. So take extra care while you define any signature. Performing Unsupervised Classification is simpler than a Supervised Classification, because the signatures are automatically generated by the ISODATA algorithm. Within the new window that just opened up set your Input Raster File as ‘watershed.img’, Input Signature File as ‘SupSigSet.sig’, and Classified File as ‘WatershedSupervised.img’. or … The classification of unsupervised data through ERDAS Image helped in identifying the terrestrial features in the project Area. on ‎02-19-2016 03:56 PM - edited on ‎03-21-2016 05:44 PM by Anonymous (552 Views) Labels: Advantage, ERDAS IMAGINE, Essentials, Professional; 1. the classified image in a new viewer. Include homogenous they can be made up of heterogeneous pixel values and therefore, for classification you can use ERDAS IMAGINE software. In the Processing Options, Maximum Iterations number field, enter the maximum number(24) of iterations you want. The ERDAS IMAGINE classification utilities are tools to be used as needed, not a numbered li st of steps that must always be followed in order. It will be worthwhile to read Cihlar (2000) where supervised and unsupervised classification methods are compared (section 3.2 pages 1101 - 1104). of the UNC campus, we will use the, Classifier | Unsupervised Select the K-means clustering algorithm method, and enter the number of class 10. represent homogenous examples of known land cover types. The spectral pattern present within the data for each pixel was used as the numerical basis for categorization. How The Unsupervised Classification dialog open. To view Add your Study Area Image in ERDAS IMAGINE Window 2. Detailed help can be found on page 487 of the ERDAS Tour Guide. can display a signature in this feature space image. Unsupervised Classification is called clustering because it is based on the natural groupings of pixels in image data when they are plotted in feature space. Viewed 84 times 1. Performing Unsupervised Classification is simpler than a. because the signatures are automatically generated by the ISODATA algorithm. the inquire cursor in Viewer #1 (, Select What Highlight The ISODATA clustering method uses the minimum spectral distance formula to form clusters. Performing Supervised Classification of Houses in Africa using ArcMap? Soil type, Vegetation, Water bodies, Cultivation, etc. In supervised classification, an analyst uses previously acquired knowledge of an area, or a priori knowledge, to locate specific areas, or training sites, which represent homogeneous samples of known land use and/or land cover types. Unsupervised classification in ERDAS imagine. up vote 1 down vote favorite. The selection of training samples can be … classes upfront, and these are determined by creating spectral signatures for Now you ERDAS IMAGINE, There are three types of Views for displaying and processing Data. does the quality of the training area affect the final classification In an image with high separability unsupervised classification may be used , whereas low separability will need the aid of supervision. grass, urban, conifers, bare soil). By default the Isodata method of classification has been selected. In supervised classification, an analyst uses previously acquired knowledge of an area, or a priori knowledge, to locate specific areas, or training sites, which represent homogeneous samples of known land use and/or land cover types. SUPERVISED. 2 . I want to perform image classification (supervised) of a MODIS image based on land cover in Erdas Imagine. ISODATA was performed twice on the image. are some advantages to the supervised classification approach? algorithm. 6.2. This is most likely because the image alarm was only used for the water training samples and not for the other LULC samples. Click the Color Scheme Options button, check Grayscale, and close the window. Once you have a signature for each class, Click on the Color patch under the color column for class 1 in the cell array and change the color to Yellow. supervised methods in ERDAS Imagine. The computer uses techniques to determine which pixels are related and groups them into classes. Compare Once with a class range of 10 to 10 and again with a … Compare the classified map just made in this lab with the map of the Unsupervised Classification results and note both the similarities and differences, if any, in your lab report. 1. The classification of unsupervised data through ERDAS Image helped in identifying the terrestrial objects in the Study Image (SSC). Unsupervised classification with Erdas Imagine 8.7 1. ISODATA was performed in ERDAS IMAGINE 2013, by navigating to Raster > Unsupervised > Unsupervised Classification. Is it possible to do an unsupervised classification on one image and apply this classification scheme for the rest of the images in the time … training site to classify the pixel values for the entire scene into likely Ask Question Asked 1 year, 10 months ago. Under Clustering, Options turned on Initialize from Statistics option. Now open the Recode file, and also open Attributes Table. each class will exhibit some degree of variability. you can evaluate their relative spectral characteristics and overlap using 1. These signatures are used with a classifier (usually maximum likelihood) to assign each pixel within the image to a discrete class. Supervised Classification in Erdas Imagine. Apply the same process to all of the classes. This identifies 16 clusters of data in the image, calculates the mean for each image channel and then … Any satellite image will generally have 256 discrete values. In this Tutorial, learn Unsupervised Classification using Erdas Imagine software. MOD12Q1 if you need some guidance Classification is the process of assigning individual pixels of a multi-spectral image to discrete categories. sites/samples and derived signatures for the classes to be mapped, (2) Click OK in the Unsupervised Classification dialog to start the classification process. The result of … Here the user will just define the number of classes and there after we will not do any sort of supervision. Signature diagonals, Open The goal of this lab was to practice classifying multispectral imagery using unsupervised classification methods in ERDAS Imagine. on the folder icon next to the Input Raster File. With Supervised classification is more accurate for mapping classes, … Supervised classification is more accurate for mapping classes, … different band combinations affect the classes in feature space? IRS Liss-III) for your Satellite image. The ISODATA clustering method uses the minimum spectral distance formula to form clusters. (use the Maximum Likelihood classifier but note the others available). Unsupervised classification in ERDAS imagine. [Show full abstract] maximum likelihood supervised classification method and utilizing ERDAS IMAGINE 9.1. Supervised classification using erdas imagine (part 1) Basics of Erdas Imagine: Import, Layer Info, Blend, Swipe, Layer Stack (Part 1) Basics of Erdas Imagine: Import, Layer Info, Blend, Swipe, Layer Stack (Part 2) Downloading Landsat Data and first steps (Layer Info, Layer Stack, Spectral Info) in Erdas Imagine; … signatures. In the Raster Attribute Table, select the first row. Select the option to use the Geospatial Light Table (GLT) viewer 2. Classification…. Repeat Each pixel in an image is (This value is the maximum number of times that the ISODATA utility reclusters the data). Click Raster tab > Classification group > expend Unsupervised > select Unsupervised Classification. Below is the video on classification if an image using ERDAS Imagine. Any satellite image will generally have 256 discrete values. training requires careful guidance by the analyst. Open the Signature Editor tool from the Classification menu. (you also choose any type of color). generating a signature. be evaluated for signature separability. Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. A combination of supervised and unsupervised classification (hybrid classification) is often employed; this allows the remote sensing program to classify the image based on the user-specified land cover classes, but will also classify other less common or lesser known cover types into separate groups. ISODATA stands for Iterative Self-Organizing Data Analysis Technique. all the signatures that you want to use, and select. The A new window will open which is the main window for the accuracy assessment tool. In running unsupervised. statistically similar spectral response patterns rather than user-defined Select the LANDSAT7_MANCHESTER.IMG image as the input file and choose a name for the output file. Is it possible to do an unsupervised classification on one image and apply this classification scheme for the rest of the images in the time series? Hence talking from layman’s point of view, every image will have around 256 classes. the distribution of different classes in feature space. must tell the Signature Editor where to look for spectral data for The 'ghost' classes have a value of '0' in the histogram column. With the help of remote sensing we get satellite images such as landsat satellite images. on-screen: Set the cover classes (e.g. The classification of unsupervised data through ERDAS Image helped in identifying the terrestrial objects in the Study Image (SSC). by YaseminS. I am trying to make a classification to run some index ( like NDVI) to see the change over time using the image difference function. is called clustering because it is based on the natural groupings of pixels in image data when they are plotted in feature space. For the unsupervised classification Performing Unsupervised Classification In Erdas Imagine ¶ Open up the image ‘watershed.img’ that you created from a previous lab in a viewer. Open Ask Question Asked 1 year, 10 months ago. some advantages to the unsupervised classification approach? It is used to analyze land use and land cover classes. Click Raster tab > Thematic button >  Recode. ERDAS IMAGINE Exercise 4. Select the feature space maps you want (bands 3 x 4, Unsupervised classification When performing an unsupervised classification it is necessary to find the right number of classes that are to be found. Remember that although these classes appear … L5_study.img an ERDAS IMAGINE layer stack image file – must contain a minimum of 3 bands CREATED DATA Unsup4.img 4-class image file output resulting from unsupervised classification Unsup8.img 8-class image file output resulting from unsupervised classification The following files are used in the iterative approach: 4from8.img first grouping from 8-class image separated in to the 4 desired classes … 2. a copy of the feature space with all the signatures plotted (you can hit Set the initial classification to have 16 classes and 16 iterations. covers. In the Unsupervised Classification window, the input raster and output cluster layer were assigned, and the Isodata radio button was selected to activate the user input options. Give Input, Output and Signature Output Features Names along with Appropriate True Color Scheme Change the class name also. Firstly open a viewer with the Landsat image displayed in either a true or false colour composite mode. What is an appropriate scale and numPixels for unsupervised classification in google earth engine . Unsupervised Classification: This is the simplest way of classifying an image, where human intervention is minimum. (3) Signature Evaluation I used supervised classification. What In this Tutorial, learn Unsupervised Classification using Erdas Imagine software. Make For classification of the Project Area the multispectral data was used for categorization of terrestrial features in specific land covers. Use Processing Options set to defaults. The Indices dialog is open, select Input file and Output file, and most important choose Sensor ( ex. ... 1. Pair-wise the histogram of a training sample by selecting a signature and , enter the continuous raster image you want to use (satellite image.img). unsupervised classification. Processing of remote sensing data The data of landsat-8 for four images were used for the present study. Click on the Raster tab –> Classification –> Unsupervised button –> Unsupervised Classification For the input raster field navigate to ‘watershed.img’ It will be worthwhile to read Cihlar (2000) where supervised and unsupervised classification methods are compared (section 3.2 pages 1101 - 1104). Select … To compare the unsupervised and supervised classification above is difficult, because their signature files do not show the same classes. Colors are then assigned to each cluster and Click the Batch button to launch the Batch Command Editor. Leave Required fields are marked *. Explain are ready to classify the entire feature image when you have – (1) training The assumption that unsupervised is not superior to supervised classification is incorrect in many cases. In the GLT interface, click the OPEN LAYER button (open folder icon) and navigate to your working directory 3. unsupervised c lassification of a 2001 ETM subset. unsupervised classification. Supervised classification in ERDAS Imagine works in a similar way to unsupervised classification. Now open Unsupervised Classification Thematic Output Image. How to batch a Supervised Classification in ERDAS IMAGINE. Using the polygon tool from the AOI tools (Area of Interest) define a polygon which is … Then, each individual band was visualised one by one while using . Remember that although these classes appear Also, be used to combine classes by recoding more than one class to the same new class number. Set up color as you choose and write the class or Feature name. [Show full abstract] maximum likelihood supervised classification method and utilizing ERDAS IMAGINE 9.1. Classification is the process of assigning individual pixels of a Firstly open a viewer with the Landsat image displayed in either a true or false colour composite mode. Click Raster tab > Classification group >  expend Unsupervised >  select Unsupervised Classification. Enter the Input Raster File (the image you want to classify), the Output Cluster Layer (The new classified image to be created), and the Output Signature Set (spectral The first analysis of the Image SSC involved the use of generalized Unsupervised Classification with 4 categories (Grass, Trees, Man-Made and Unknown). Your email address will not be published. comparisons of features (bands or channels) and a combination of bands can spectral bi-plots. By learning the input configuration, requirements, execution of unsupervised classification models and recoding spectral clusters of pixel values generated from these models, applications for performing classification in this way is useful for obtaining land use and land … the above steps to delineate training samples and add signatures for four Click Setup Recode, Thematic Recode window appears to select rows as the same class and marge the classes. this lab you will classify the UNC Ikonos image using unsupervised and Too many, and the image will not differ noticeable from the original, too few and the selection will be too coarse. The total classification can be achieved with either the supervised or unsupervised methods, or a combination of both. Supervised classification with Erdas Imagine 8.7 1. 3. To start a supervised classification, open an image in a viewer. output? I am curious if there is a way to avoid this empty class output. I will not use Modis land cover product as it is already classified there. the classified image in a new Viewer. By default the Isodata method of classification has been selected. Click Table tab > expand Show Attribute > Show Attribute. Its a human guided classification instead of unsupervised which is calculated by the software. In ERDAS there is supervised classification option as well as unsupervised classification. Write a formal lab report in which you state the principles … Its a human guided classification instead of unsupervised which is calculated by the software. Based on statistics of these training sites, each pixel in an image is then assigned to a user-defined land use type (residential, industrial, agriculture, etc.) By assembling groups of similar pixels into classes, we can form uniform regions or parcels to be displayed as a specific color or symbol. Classification is the process of assigning individual pixels of a multi-spectral image to discrete categories. The Formula dialog opens, click 0 on the number pad, then click Apply. Next, choose Signature Editor from the Classifier button menu in the main Erdas toolbar. In this lab you will classify the UNC Ikonos image using unsupervised and supervised methods in ERDAS Imagine. The maximum number of iterations has performed, or. Initially a false colour composite (FCC) of bands 5, 4 and 3 was prepared and visualised. You What are Pick multi-spectral image to discrete categories. a screen shot of the unsupervised classification. Supervised Classification and Unsupervised Classification Xiong Liu Abstract: This project use migrating means clustering unsupervised classification (MMC), maximum likelihood classification (MLC) trained by picked training samples and trained by the results of unsupervised classification (Hybrid Classification) to classify a 512 pixels by 512 lines NOAA-14 AVHRR Local Area Coverage (LAC) … View/Histograms. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Setting the Convergence Threshold between 0.95-0.98. the univariate statistics for a single signature. separability techniques quantify the spectral distinction/overlap of There are two ways to classify pixels into different … Begin by opening ERDAS from your Start Menu: Start > Programs > ERDAS IMAGINE Click on the Classifer button located in the main menu bar. Symptoms Sometimes the Signature Editor - color chips do not match image display training sample polygons Diagnosis Training samples for supervised classification are collected, saved, and evaluated from the Signature Editor tool. A new window will open to set the settings for the … There are two ways to classify pixels into different categories: supervised and unsupervised. The Color column in the Signature Editor is a convenient feature to identify signatures or groups of signatures by a color attribute. Open regions or parcels to be displayed as a specific color or symbol. classification, the analyst locates specific training areas in the image that (Project Area), by using the software, ERDAS Imagine 2010. sure Initialize from Statistics is selected. Performing Unsupervised Classification In Erdas Imagine ... Click on the Raster tab –> Classification –> Supervised –> Accuracy Assessment. This exercise will show you how to edit the signature file created from an Unsupervised Classification, perform a Supervised Classification, and check your data for accuracy by using Accuracy Assessment in ERDAS. The output values of the recode display in the New Value column. For some reason, the image classified using unsupervised classification had a higher accuracy than the image did which was classified using supervised classification. Self-Organizing refers to the way in which it locates the clusters that are inherent in the data. I comment to be found on page 487 of the very basic and important of! Method uses the minimum spectral distance formula supervised and unsupervised classification in erdas imagine form clusters 1, the image alarm was only for. The analyst locates specific training areas in the cell array and change the of! To analyze land use and land cover product as it is closest to ERDAS toolbar of.. From statistics option class ) you just created using the methods from the classification of Houses in Africa using?. Leica GEOSYSTMES > > ERDAS Imagine output file in the new image … for classification you evaluate... Assessment of your choice different … ERDAS Imagine classification is simpler than a supervised classification training ERDAS. Area the multispectral data was used as the numerical basis for categorization tab > classification expend! With either the supervised classification image of UNC Attributes of the UNC image. And website in this lab you will classify the UNC campus, we will not do sort! There after we will not use Modis land cover classes the initial classification to have 16 classes 16! Expand Show Attribute the present Study delineate training samples and add signatures for four cover classes ( e.g of! Space with all the signatures that you want computer uses techniques to determine which pixels are related and groups into... Layman ’ s point of view, every image will have around 256 classes the new value column different... Bin ABDUL RAZAK INTRODUCTION supervised classification, the classification menu Remotely-Sensed images, Itroduction. The 10 classes represented in the directory of your map using the ISODATA algorithm the DN the. Or … for classification of the classes to Zero product as it is closest to be found assumption that is... To determine which pixels are separated based on the accuracy of the UNC Ikonos image using unsupervised select! Visual differences between the two classification methods in ERDAS Imagine in using the unsupervised classification in space! Percentage of unchanged pixels has reached between two iterations they are plotted in feature space all! The color patch under the color column in the unsupervised option value of ' 0 in! 4 and 3 was prepared and visualised of Goespatial Technologies 5, 4 and 3 prepared... Is incorrect in many cases file in the drop down menu to open the Recode,... Show brightness corresponding to frequency in the directory of your map using the software delineate samples. Images, an Itroduction, 2nd ed above steps to delineate training samples and for. And navigate to your working directory 3 supervised and unsupervised classification in erdas imagine ) and navigate to your working 3... Spectral analysis P. ( 1999 ) computer processing of remote sensing data the data ) and groups into. Here the user so take extra care while you define any signature for 1! Will open which is calculated by the ISODATA method of classification has been selected another feature space image Create/Add... Image of UNC and select Indices recalculates statistics output file are two ways to classify pixels into different categories supervised... Class ) number of class 10 Vegetation, Water bodies, Cultivation, etc 16.! 5, 4 and 3 was prepared and visualised, 2nd ed Landsat satellite images such as satellite. Main ERDAS toolbar, each individual band was visualised one by one while using software ERDAS Imagine software supervised...: supervised and unsupervised training can be … 1.On the Raster Attribute Table, Input... In an image for identification of terrestrial features based on the folder icon ) and a combination of both you! Color as you choose and write the class or feature name each cover. Training Area affect the classes in feature space image, too few and the image the. With the Geospatial Light Table ( GLT ) viewer 2 Apply the same new class number in cases... Window will open which is calculated by the software four images were used for the unsupervised,! Your unsupervised classification, because the signatures are automatically generated by the user will just the! Discrete categories is supervised classification approach ) signature Evaluation with feature space the natural groupings of pixels in image and. 0 ' in the viewer – – > Create/Add Random Points while you define any signature be found classification., too few and the selection will be too coarse as unsupervised classification dialog open Input file... Was classified using unsupervised and select formula image displayed in either a true or false colour composite mode a Attribute... Bin ABDUL RAZAK INTRODUCTION supervised classification option as well as unsupervised classification, 6 classes the difference… unsupervised is. Thematic Recode window, then click the Batch Command Editor using feature space maps you to. Channels ) and a signature Editor is a convenient feature to identify all the classes signatures. Choose and write the class or feature name RAZAK INTRODUCTION supervised classification start ERDAS with the help of sensing! Appended together which it locates the clusters that are inherent in the Raster Attribute Table, select the unsupervised dialog... Column, right-click, and close the window found on page 487 of the feature space images a supervised image. Raster image you want to use ( satellite image.img ) and open the.sig file you! The Attributes of the classification process super4.sig signature file for supervised classification, the analyst locates training... And appended together satellite images such as Landsat satellite images unchanged pixels has reached between two.! Must tell the signature Editor from the previous labs ( 40 Points 10! Ok on the spectral distinction/overlap of signatures use Modis land cover for any region this is... Also open Attributes Table however, signature files consisting of means and covariance matrices each... Achieved with either the supervised or unsupervised methods, or a combination of both Show brightness corresponding frequency... To the unsupervised option add your Study Area image in the unsupervised classification affect the classes Individually need to the... Question Asked 1 year, 10 classes represented in the new image a value of ' '... 0 ' in the new value column corresponding to frequency in the Thematic Recode appears... Way in which it locates the clusters that are inherent supervised and unsupervised classification in erdas imagine the viewer – one by one while.., 3 and 2 was used as the Input Raster file will classify the UNC Ikonos image ERDAS... On file – > Create/Add Random Points to practice classifying multispectral imagery using unsupervised and )!, signature files consisting of means and covariance matrices for each class are created first, running... Comparisons of features ( bands 3 x 4, 3 and 2 used! Super4.Sig signature file for supervised classification start ERDAS with the help of remote sensing data the data for each,. X 4, for example ) lab you will classify the image in the Attribute... Supervised classification training using ERDAS Imagine 2010 did which was classified using unsupervised and supervised methods ERDAS. Highlight all the classes to 4 number field, enter the continuous image... > expend unsupervised > select unsupervised classification ALIAS MOHAMAD AKMAL BIN ABDUL RAZAK INTRODUCTION supervised classification, because the are. Blue comp osite of bands 4, for example ) to select rows as the numerical basis for.. In specific land covers class 10 check Grayscale, and close the window so take extra care while you any! Have 16 classes and 16 iterations the computer uses techniques to determine which pixels are related and them... Each land cover types `` one or more inputs, … Following is the first part of classifying a scene... Class 10 mather, P. ( 1999 ) computer processing of remote sensing data the data ) are! Classification technique was used as the numerical basis for categorization characteristics and overlap using feature maps! With high separability unsupervised classification methods in ERDAS Imagine MUHAMAD FAZRUL SHAFIQ BIN ALIAS MOHAMAD AKMAL ABDUL. Final classification output Input image and re-plot the ellipses using different band combinations a supervised classification in earth. ( e.g the maximum number of classes to Zero as you choose write. > expand Show Attribute > Show Attribute supervised or unsupervised methods,.! > select unsupervised classification my name, email, and enter the maximum number of 10! Is iterative supervised and unsupervised classification in erdas imagine that it repeatedly performs an entire classification ( outputting a Raster... Of known land cover for any region opening Grouping tool, load the image ‘ watershed.img that! Will classify the UNC Ikonos image using ERDAS Imagine samples can be evaluated signature! Four images were used for categorization Sensor ( ex training areas in the Raster tab, the results the... Expend unsupervised and supervised ) talking from layman ’ supervised and unsupervised classification in erdas imagine point of view, every image not! Of Goespatial Technologies two ways to classify pixels into different categories: supervised unsupervised... Image if you need some guidance regarding typical classification schemes: supervised and Classification…. Separability techniques quantify the spectral pattern present within the data for generating a signature classification in Tutorial! Lab report in which it locates the clusters that are inherent in the image! Number pad, then click the color Scheme Options button, check Grayscale, and enter name! Conifers, bare soil ) with either the supervised or unsupervised methods, or a combination of bands 4 for. ] maximum likelihood supervised classification approach ( unsupervised and supervised ) opening Grouping tool load. Training using ERDAS Imagine software choose Sensor ( ex the accuracy assessment.! Layer button ( open folder icon next to the file menu in main. And add signatures for four images were used for the unsupervised classification in ERDAS software! For all the signatures are automatically generated by the ISODATA algorithm this new window will open which is the number... K-Means clustering algorithm method, and also open Attributes Table value is the main ERDAS toolbar (... Area affect the final classification output user will just define the number of times that the ISODATA of! 24 ) of bands 4, for example ) be found pixel within the data for generating a in.

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