DEM generation using Cartosat -1 stereoscopic data

Workshop on

expert opinion in landslide inventory and hazard and risk mapping”, mapping”,

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January 3 -19, 2008

DEM generation using Cartosat -1 stereoscopic data

Prepared by

Tapas Ranjan Martha Scientist ‘SD’ [email protected]

Geology and Geophysics Division (Earth Resources Group) RS & GIS APPLICATIONS AREA NATIONAL REMOTE SENSING AGENCY DEPT. OF SPACE, GOVT. OF INDIA HYDERABAD-500 037

Indian Institute of Remote Sensing (NRSA), Dehradun, India

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January 3 -19, 2008

This document explains the procedure / steps to be followed for generation of Digital Suface Model (DSM) and orthoimages by using the stereoscopic data of Indian Remote Sensing satellite Cartosat – 1. The steps for DEM generation and orthoimages are applicable to Leica Photogrammetric Suite (LPS) software only and hence will be different, when some other software is used. This document is exclusively prepared for the training course & workshop on “expert opinion in landslide inventory and hazard and risk mapping” at IIRS, Dehradun, India during January 3 -19, 2008.


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Prepare the Block File Here we will create a block file by using the stereoscopic Cartosat-1 data of Okhimath, Uttarakhand, India. This dataset was acquired in April, 2006. 1.

Click the Erdas Imagine icon on the desktop.

2.

Click the LPS icon on the Erdas Imagine icon panel

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The LPS Project Manager opens. Acess LPS tools using the toolbar This is the block project tree view – make choices here to view them in the project graphic status window

This is the project graphic status window-you may control the contents with the tools on the right side of the dialog

Images in the block file appear here in the cell Array

3.

Click the Create New Block File icon

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The Create New Block File dialog opens.

Click OK

Enter the name of the new file and then click Enter on the keyboard


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Type the name of the file as training_okhimath. Click OK to close the Create New Block File dialog.

Once you have created a block file, the Model Setup dialog opens. Here, you select a geometric model that corresponds to the type of camera associated with the images.

Select Geometric Model 1. Click the Geometric Model Category dropdown list and select Rational Functions.

Click here and select Rational Functions

2.

Click CARTOSAT RPC in the Geometric Model list.

3. Click OK to close the Model Setup dialog.

Now, the Block Property Setup dialog opens.


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Define Block Properties Click Set button to select project chooser

1. Click the Set button in the Horizontal Reference Coordinate System section of the Block Property Setup dialog. 2. Click the Custom tab in the Projection Chooser dialog.

3. 4. 5. 6. 7. 8.

Click the Project Type dropdown list and select UTM. Click the Spheroid Name dropdown list and select WGS 84. Click the Datum Name dropdown list and select WGS 84. Input UTM Zone as 44. Input NORTH or SOUTH as North. Click OK.


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9. Now, Block property setup dialog will appear as below.

In this example, the block file is in WGS84, so the vertical coordinate system is also WGS84 by default. If the vertical system is different, you must change it to match the block file. 10. Click OK

Add Imagery to the Block Now that you have provided general information about the block, you can add images and create pyramid layers. 1. Select Edit -> Add Frame from the menu bar, or click the Add Frame icon The Image File Name dialog opens.


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2. Navigate to the workspace and select BANDA.TIF. 3. Hold down the Shift key and select BANDF.TIF. Both the images are selected and can be added to the block file at once.

4. Click OK to close the Image File Name dialog. 5. Click the plus sign

next to the Images folder to see the list of images (banda.tif and bandf.tif) in the training_okhimath.blk file.

Now, you will see that pyramid and online is appearing in green colour in the cellArray.


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Check the Frame Properties 1.

On the LPS toolbar, click the Frame Properties icon

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The CARTOSAT RPC Frame Editor dialog opens the Sensor tab, and displays the first Cartosat-1 image in your block file. Like other types of block files, you can use the Next button to move through the images in the block to ensure that the appropriate RPC file is associated with each image. If the RPC is located in the same directory as the image file, LPS Project Manager loads it for you. 2.

If it is located in a folder other than the one that contains the Cartosat-1 images,

3.

click the Open icon and navigate to the directory containing the RPC file for the first image. Select the file and click OK in the File Selector.

Notice that the RPC Coefficients file name matches that of the Image File Name. The minimum and maximum elevation and meters defaults are derived from the metadata file that accompanies Cartosat-1 images.

What is RPC? RPC (rational polynomial coefficient) files contain rational function polynomial coefficients that are generated by the data provider (e.g. NRSA for cartosat-1 data) based on the position of the satellite at the time of image capture. Cartosat-1 RPC files (banda_rpc.txt and bandf_rpc.txt) are encrypted metadata files. Hence, it is not readable. 4.

Click the Next button to see the second image in the block file.

5.

Again, if necessary, click the Open icon and navigate to the directory containing the RPC file for the second image. Select the file and click OK in the File Chooser. Click OK in the CARTOSAT RPC Frame Editor dialog to accept the images and their associated RPC files.

6. 7.

You are returned to the LPS Project Manager. The RPC file provides enough information to approximate the interior and exterior orientation parameters (for some other image types GCPs are required); therefore, those columns are green in the CellArray.


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Start the Point Measurement Tool The Point Measurement tool is designed to collect GCPs and tie points that are common to two images so that the images can be correlated. In the case of Cartosat-1 imagery, the RPC file that comes with the images contains valuable information that lessens the need to collect large numbers of control points. 1. On the LPS toolbar, click to select the Point Measurement icon 2. Select classic point measurement tool.

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The Point Measurement tool opens, displaying the two Cartosat - 1 images in the block file.

Add Control Points You can select well-distributed control points on each of the images with which to improve the correlation between the Cartosat-1 images. In the following steps, ground control point coordinates (X, Y and Z) collected during DGPS survey are added to the reference CellArray. 1. Click the Add button. 2. Locate the points for the DGPS survey was done and place them in the two images (left and right) using the Create Point tool 3. Click in the Description cell and type U-15. 4. Click the Type cell and select Full. 5. Click the Usage cell and select Control.


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6. Using DGPS values, input the X, Y, and Z Reference values into the reference CellArray. Now, you have successfully added one control points. You have to add 2 -3 more well spread points for achieving better final results. 7. So, in the Point Measurement tool, click the Add button to add another point. A new Point # is added to the reference CellArray at the bottom of the Point Measurement tool. 8. Follow steps 2 to 5 again and add all the control points. The above steps from 1 - 8 requires more time for careful observation of the GCPS. For the purpose of the exercise, the control points can be imported from the available file. on the point measurement dialog. 9. Click import/export points icon 10. Click the radio button Block File (.blk) in select point source category.

11. Click Ok. 12. Now, the select the block file in the new dialog and click OK.


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Start Auto Tie LPS Project Manager can automatically select tie points in your block file with the help of the control points you just selected. Tie points can also improve your final orthoimages. 1. In the Point Measurement tool, click the Auto Tie Properties icon The Automatic Tie Point Generation Properties dialog opens on the General tab.

2. Click Distribution in the dialog.


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3. Check Defined pattern circle. 4. Input 1200 instead of 2400 in column increment and line increment (This pattern depends upon the terrain type and contrast between various image objects). 5. Click Run in the dialog. 6. Auto tie summary dialog will appear as below.

This is acceptable success rate 7. Click Close in the dialog. When the process is complete, the tie points are appended to the bottom of the reference CellArray in the Point Measurement tool.


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8. Click in the > column of one of the points to see its location in both images. Now, you will notice that, the tie points are well distributed over the image. Most of the points on the left and right images are matching. If there are few unmatched points, then delete them. For the sample data sets used in the exercise, this method of tie point generation works out well. 9. When you are finished, click the Save button in the Point Measurement tool.

Block Triangulation During triangulation with Cartosat-1 data, you can refine the sensor model with polynomials, which are contained in the RPC file you specified in the Frame Editor dialog. Using these coefficients along with the GCPs creates a more accurate orthoimage. 1. Click the Triangulation Properties icon

in the Point Measurement tool.

The Rational Function Refinement dialog opens.


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Click the checkbox to refine with polynomials. This improves the solution

2. Make sure the Refinement with Polynomials checkbox is checked. By using this option, you apply polynomial corrections to the original rational function model. This option corrects remaining error and refines the mathematical solution. 3. Click in the Polynomial Order field and type 1, or use the increment nudgers. 4. In the Rational Function Refinement dialog, click the Run button. The Refinement Summary dialog opens.

5. Click Accept in the Refinement Summary dialog. This RMSE error is quite large and in order reduce it click Run and Accept few more times till you get results like this as displayed below. This is an iterative process in LPS and with this the model converges thereby the RMSE reduces.


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Now you will see that the total RMSE is not reducing further. Hence we have to see the triangulation report and delete the points, which are contributing towards the error. 6. Click Report in the Refinement Summary dialog.

7. Scroll down the report and see the points 14, 49 and 100 are contributing most towards error. 8. Delete those points from the cellArray of the point measurement dialog. 9. Now, click on the triangulation properties. 10. Click in the Polynomial Order field and type 1. 11. Click Run.

12. Try this method of eliminating erroneous points till you get RMSE value less than 1 pixel. The X Reference, Y Reference, and Z Reference cells corresponding to the Tie points update. 13. Click Save in the Point Measurement tool.


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DTM Extraction Now, set the properties for extraction of the DTM from the Cartosat-1 data. 1. From the LPS toolbar, select the DTM Extraction icon Extraction.

, or select Process | DTM

The DTM Extraction dialog opens. It allows you to select the type of DTM you wish to create, as well as the parameters to apply during the extraction of the DTM.

By default, the Output DTM Type is DEM. 2. In the Output Form section, click the radio button next to Individual DTM files. 3. In the Output DTM File section, click the Open icon training_okhimath and then click Enter 4. Change the DTM Cell Size in X and Y if you wish. 5. Check the box Make pixels square 6. Click the Advanced Properties button on the DTM Extraction dialog.

and

input

The DTM Extraction Properties dialog opens on the General tab. The General tab has information regarding the projection and units, which are inherited from the block file.


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7. Click the checkbox next to Create Contour Map. The Contour Map is in Shape file format, and is designated with "_contour" appended to the file name. 8. In the Contour Interval field, change the value to 20 (Cartosat-1 data supports up to 10m contour interval). 9. Click the checkbox next to Create DTM Point Status Output Image. The DTM Point Status image is in ERDAS IMAGINE .img format, and is designated with "_quality" appended to the file name. 10. Click the Image Pair tab. The Image Pair tab is used to evaluate image pairs and determine which image pairs you want to use for automated DTM extraction. Only those image pairs that overlap by the specified overlap threshold are active. 11. Click the View icon

to open the views at the top of the dialog

12. Click the Area Selection tab. The Area Selection tab is provided so that you can specify which geographic regions contained within an image pair are used for DTM extraction, or are not used for DTM extraction. You can create custom strategies applicable to particular regions to improve the accuracy of your output DTM. 13. Click the View icon

to open the views at the top of the dialog.


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Notice that the OverView displays the extent of the correlation area in red, and the default overlap in white. This helps you to digitize regions in the Area Selection tab for the purpose of applying specific strategies. 14. Change Region strategy to high mountains. 15. Click the Accuracy tab. 16. Click the checkbox next to Use Block GCPs. 17. Click the View icon

to open the views at the top of the dialog.


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It means the GCPs collected from DGPS survey will be used to check the DTM accuracy 18. Click OK in the Accuracy tab. You are returned to the DTM Extraction dialog.

19. Click Run in the DTM Extraction dialog to start the process. When the extraction is complete, the DTM column in the LPS CellArray becomes green.

View the DEM and Contours 1. 2. 3.

Click the VirtualGIS icon on the Erdas Imagine icon panel. Click VirtualGIS viewer. Open the DEM image file (training_okhimathbanda_bandf.img) to see.

Now you will see that some errors such as pits and spikes are there in the DEM. You have to correct those errors by manual DTM editing. Another method is generating the DEM manually by applying break lines. This is a tedious process and not covered in this practical exercise. But it is essential to correct for those DEM errors before proceeding to ortho image generation.


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Ortho Resample the Images Ortho resampling of the image is required to remove the geometrical distortion to the image due to the topography 1.

2.

Click in the Ortho column to open the Ortho Resampling dialog, or click the Ortho Resampling icon . You are prompted as to whether or not you want to delete the orthoimage corresponding to the first image you created in the first part of this tour guide. Click No in the Attention dialog.

The Ortho Resampling dialog opens.

3. 4. 5. 6.

Click in the Active Area field and type 90.0, or use the increment nudgers to the right of the field. Click in the Output File Name section and type a new name for the output orthoimage (training_okhimath_ortho). Click the DTM Source dropdown list and select DEM. The DEM you have created will the selected automatically, if not, then select the DEM from the file browser.


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In this exercise, the DEM created by manual process through breaklines is used (provided for this exercise purpose). 7. 8. 9. 10.

Click the Advanced tab in the Ortho Resampling dialog. Click the checkbox next to Ignore Value, and maintain the value of 0. Click OK in the Ortho Resampling dialog to start the ortho resampling process. Drape the orthoimage over the DEM in the VirtualGIS viewer and see the terrain.

Comparison between DEMs In the normal viewer, open the two DEMs created by manual and automatic method in LPS using Cartosat-1 data. Also open the SRTM DEM for this area in another viewer and make a comparison.

Problems of the DEM created with LPS 1. Automatic tie point generation in LPS is based on image matching technique. So, it does not produce the desired points if there is not sufficient image contrast. Hence tie point properties have to be modified suitably. 2. Shadow areas in high mountainous regions creates problem in triangulation and DEM generation. 3. Manual method of DEM extraction through breaklines gives better results.

Bibliography 1. 2. 3.

4.

Leica Photogrammetry Suite Project Manager User's Guide Leica Photogrammetry Suite IKONOS Sensor Model Support Sadasiva Rao, B., Murali Mohan, A.S.R.K.V., Kalyanaraman, K. and Radhakrishnan, K., 2006. Evaluation of Cartosat-I Stereo Data of Rome, International Symposium on Geospatial Databases for Sustainable Development. ISPRS TC-IV, Goa, India. Wise, S.M., 2007. Effect of differing DEM creation methods on the results from a hydrological model, Computers and Geosciences, 33, pp. 1351-1365

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