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ALOS PRISM

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ALOS stands for Advanced Land Observing Satellite and was developed by the Japan Aerospace Exploration Agency (JAXA). The sun Synchronous, Sub recurrent ALOS was launched by JAXA in January of 2006 from Tanegashima Space Center in Japan. The purpose of ALOS was to provide valuable information for mapping, precise regional land coverage observation, disaster monitoring, and resource surveying. ALOS contains three sensors, commonly referred to as the “three eyes” of ALOS. These sensors are: the Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM), the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), and the Phased Array type L-band Synthetic Aperture Radar (PALSAR).

The PRISM sensor onboard ALOS contains three independent optical systems that allow for viewing in the Nadir direction, as well as forward and backward directions. This allows for the production of a stereoscopic image along the satellite’s track.  PRISM contains 1 band (panchromatic) with a wavelength of
0.52 to 0.77 micrometers. The spatial resolution of PRISM is 2.5m (when viewing in the Nadir direction). Swath width of PRISM is 70km when viewing in the Nadir direction, and 35km when in triplet mode. Prism’s spatial resolution makes this sensor particularly desirable for mapping, urban planning, and monitoring desired areas. PRISM cannot image regions that are beyond 82 degrees North latitude and 82 degrees South latitude.

The PRISM sensor contains 6 CCDs for viewing at the Nadir, and 4 CCDs for viewing in the forward and backward directions. An image file is provided for each CCD when dealing with 1A and 1B1 level imagery (uncorrected imagery). Geomatica software offers support for PRISM imagery levels 1A, 1B1 and 1B2R.
It is recommended to use Level 1B1 data because it is radiometrically corrected and is without geometric correction.

Rigorous Modeling


1. Initial Project Setup
Start OrthoEngine and click ‘New’ on the File menu to start a new project. Give your project a ‘Filename’, ‘Name’ and ‘Description’. Select ‘Optical Satellite Modeling’ as the Math Modeling Method. Under Options, select ‘Toutin’s Model’. After accepting this panel you will be prompted to set up the projection information for the output files, the output pixel spacing, and the projection information of GCPs. Enter the appropriate projection information for your project.

 

2. Data Input
After doing the initial project setup, you need to add both images of the stereo-pair to the orthoengine project. Under ‘Processing Step’ goto ‘Data Input’ and click on ‘Read CD-ROM’. Select ‘PRISM (LGSOWG)’ as the CD Format and VOL file as the header file. As per the raw imagery, choose the appropriate channel number(s). Provide a PIX file name and click ‘Read’. This will import the raw PRISM data into PIX format and will add it to the OE project.

The raw PRISM 1A and 1B1 level data is generally distributed in 4/6 tiles. In that case user needs to select appropriate number of channels while reading-in the data. E.g. if there are 4 tiles in the input dataset with channel 1, 2, 3 and 4, then user needs to select 1, 2, 3 and 4 as the requested channels.

 

3. Collect GCPs and Tie Points
Select the ‘GCP/TP Collection’ processing step. GCP collection can be done using various options viz. ‘Manual Entry’, ‘Geocoded Images/Vectors’, ‘Chip Database’ or a ‘Text File’.

For the ALOS PRISM Toutin’s model, a minimum of six accurate GCPs per image (or more, depending on the accuracy of the GCPs and accuracy requirements of the project) are required. After collecting the Stereo GCPs / GCPs / TPs, select the ‘Model Calculation’ Processing Step and click on ‘Compute Model’. Check ‘Residual Report’ panel (under the Reports processing step) to review the initial results.

 

4. DEM from Stereo: Generate Epipolar Images
When ‘User Select’ is chosen as ‘Epipolar selection’, selection of exact left and right image does not matter. Just select any image as ‘Left Image’ and other image will be added as the right image. Make sure to select the image under ‘Right Image’ box and click on ‘Add Epipolar Pairs to Table’ to record the pair(s) under List of ‘Epipolar Pairs’. If ‘User Select’ is chosen, repeat the steps until all stereopairs are recorded. In ‘Down Sample Factor’ put the number of image pixels and lines required to calculate one epipolar image pixel.

For PRISM data, it is also recommended that the user set a down sample factor greater then 1 to obtain a more smooth DEM. For this example, we will use a down sample factor of 2. A value of 4 may also be good.

In ‘Down sample filter’, click the method used to determine the value of the epipolar image pixel when the Down Sample Factor is greater than 1.
Select one of the following:

  • ‘Average’ to assign the average image pixel value to the epipolar image pixel. The average is obtained by adding the image pixel values that will become one epipolar image pixel and dividing that value by the number of image pixels used in the sum.
  • ‘Median’ to assign the median value of the image pixels to the epipolar image pixel. The median is obtained by ranking the image pixels that will become one epipolar image pixel according to brightness. The median is the middle value of those image pixels, which is then assigned to the epipolar image pixel.
  • ‘Mode’ to assign the mode value of the image pixels to the epipolar pixel. The mode is the image pixel value that occurs the most frequently among the image pixels that will become one epipolar image pixel.


Check off the epipolar pairs under the ‘Select’ column and then click on ‘Generate Pairs’.


5. Extract DEM
Under the DEM from Stereo processing step, select ‘Extract DEM Automatically’ button.

  • In ‘Select’ column, check off the epipolar pair from which the DEM will be extracted
  • Under the ‘Epipolar DEM Extraction Options’:
  • Enter ‘Minimum’ and ‘Maximum’ elevation values. This elevation range is used to estimate the search area for the correlation. This would increase the speed of the correlation and reduce errors.
  • If the resulting DEM contains failed areas on peaks or valleys, then try increasing the range.
  • For ‘Failure’ value, enter the value used to represent the failed pixels in the output DEM. The default is set to be -100
  • Enter a ‘Background value’ to represent “No Data” pixels that lie outside the DEM. These pixels are distinguished so that they would not be mistaken for elevation values. The default value is -150.
  • For ‘DEM Detail’, specify the level of detail desired for the output DEM. Low detail indicates that the process stops during the coarse correlation phase of aggregated pixels. High detail would mean that the process continues until correlation is performed on images at full resolution.
  • In the ‘Output DEM channel type’, enter 16 bit signed.
  • Select the desired ‘Pixel Sampling Interval’, or sampling frequency. This parameter controls the size of the pixel in the output DEM relative to the input images. The higher the number specified, the larger the DEM pixel will be and the faster the DEM is processed.
  • Under the ‘Geocoded DEM’’ section, select ‘Create Geocoded DEM’ to geocode and merge the epipolar DEMs. However if the DEM is to be edited prior to geocoding, leave this option unselected. If the option is selected, enter a file name for output DEM.
  • Click on ‘Extract DEM’ button.

 

6. Examine Results
Examine the DEM in Focus and continue editing if necessary.

Bad results in the DEM can often be caused by the data, the stereo coverage, the accuracy of the model generated from control points, etc. If there are numerous failed areas that cannot be easily corrected using the DEM Editing Tools, then try returning to OrthoEngine and generating epipolar images again or extracting DEM using different parameters (e.g. increase the down scale factor). The PCI Geomatica help files on Applying Tool Strategies for Common Situations in Digital Elevation Models contain more information about improving DEM output.

 

Rational Polynomial Coefficients (RPC)


ALOS PRISM stereopair delivered with RPC files can be used for DEM generation using Rational Fucntion project, in the absence of adequate number of GCPs. Further addition of 1-4 GCPs into your project, in addition to the delivered RPCs can significantly improve the accuracy of the DEM.


1. Initial Project Setup
Start a new project and select the math modeling method as ‘Optical Satellite Modeling’. Under ‘Options’ select ‘Rational Functions’ Extract from Image’ option.

 

2. Stitch Images
While using Rational Function project, stitch the PRISM image tiles by ‘Stitch Image Tiles with RPC’ functionality under ‘Utilities’ menu. Stitching combines different image tiles into a single PIX file and generate a new RPC for the stitched image.

 

Browse for image tiles; provide an output PIX file name in the stitching window and click ‘Stitch’ button. At the end of this process, user will be prompted to add-in the final PIX file into the OE project. Click ‘Yes’ and the PIX file will be added into the OE project.

Repeat the same stitching activity for the other set of PRISM tiles in the stereo-pair. Double check that both stitched PIX files are added inside OE project.

3.GCP Collection
At this stage an Epipolar / DEM can be directly generated in the absence of any GCPs.

If GCPs are available, they can be added into the project using the same process as defined in section 1.3 of this document.

Note: It is recommended to use 1st order RPC adjustment in a PRISM Rational Function project.

 

4. Generate Epipolar Images and Extract DEM
Generate Epipolar images and extract a DEM using the same steps as described in section 1.4 & 1.5 (under Rigorous Model) of this document. Examine the DEM in focus and continue editing if necessary.

 

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