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GOES IR Channel to Channel Co-Registration Algorithm and Implementation

GOES IR Channel to Channel Co-Registration Algorithm and Implementation. Zhenping Li@SGT 10/24/2012. Agenda . The channel to channel co-registration algorithm Fast Fourier Transformation Resampling algorithm (FFTR) Image Resampling The implementation OPS concept

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GOES IR Channel to Channel Co-Registration Algorithm and Implementation

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  1. GOES IR Channel to Channel Co-Registration Algorithm and Implementation Zhenping Li@SGT 10/24/2012

  2. Agenda • The channel to channel co-registration algorithm • Fast Fourier Transformation Resampling algorithm (FFTR) • Image Resampling • The implementation • OPS concept • IR Co-registration software package. • SPS implementation • GVAR Change

  3. IR channel to channel co-registration characterization • Uses line-by-line temperature correlation for the channel to channel co-registration approach. • Similar work done by Mike Grotenhuis@STAR • Proposed by ITT • The inputs are from the images in GVAR. • Only the earth pixels are used for the correlation. • The image size must be sufficient large. • Will include full disk, CONUS images, Southern hemisphere, Northern Hemisphere images. • Too much uncertainty for smaller size images (rapid scan), and they will not be included. • The sample size for a given line must be sufficient large, • larger than 1000 pixels after excluding the space pixels for East-West Direction co-registration • Larger than 750 pixels after excluding the space pixels for the North-South Direction. • Using channel 4 as the reference channel, calculating the mis-registration errors of other IR channels relative to Channel 4 in both East-West and North-South direction.

  4. IR channel to channel co-registration characterization • The general steps: • Converting the image into a two dimensional temperature array. • Evaluating the starting and end earth pixel for each line (East-West) or column (North to South) to exclude the space pixels. • Create the input arrays for both channel C and 4 for the earth pixels only. • The linear correlation function can be expressed as the function of mis-registration δ • TC(x) is the channel C temperature at position x, C for 2, 3, and 6. • T4(x+ δ) is the channel 4 temperature at the position x+δ • Perform resampling to evaluate T4(x+ δ). • Obtain the maximum correlation and the corresponding shift for the line or column

  5. Mis-Registration for an Image • The mis-registration for an image is obtained by calculating the weighted average over the mis-registration of each line for East-West Co-registration or each column for North-South Co-registration: • The Ri(δi) is the maximum correlation for the line i in the image. δI is the mis-registration for line i. • Only the line with Ri(δI) >= 0.8 is included in the calculation • Ri(δi) is used as the weight so that the result favors the contributions from the lager correlation

  6. Fourier Transformation and Resampling • Define a global function for the input array in Fourier Expansion form: • N is size of the input array. This approach works better for larger size array. • The resampling array is • The Fast Fourier Transformation (FFT) is used in determine the frequency amplitudes and resampling

  7. Test Case: δ=0 • Blue line is the FFT outputs, and the red dots are the input array. (N=2126) • The maximum difference between data and FFT output is 4x10-4 GVAR counts for GVAR at order of 100 counts

  8. The DFT amplitudes as the functions of frequency • The x-axis corresponds to the ratio k/M, where M is the size of the FFT. • The amplitudes converges quickly as the function of k/M • This is typical behavior for a line in the IR images.

  9. FFT Re-sampling is reversible • In the real operational scenarios, Gδ(x) for a fractional δ is obtained by rounding the FFT output (double value) into the short integer. • One can follow the same procedure to resample Gδ(x) by –δ to obtain the original array, and cast the FFT output into the short integer again. • The conversions of the double variable into the short integer in the re-sampling and reverse re-sampling processes cause the maximum 2 GVAR counts uncertainty. • The test run with δ=0.5 where the uncertainty is largest shows that the max difference of the reversed array and the original array is generally 1 GVAR counts. • The special case: • The maximum difference between data and FFT output is 3x10-3 GVAR counts for GVAR at order of 100 counts • The re-sampling in this case is reversible with no error for integer δ.

  10. The Efficiency • The resampling algorithm using FFT has the efficient of O(Nlog2N). • The algorithm is particularly good for the images with larger sizes. • The time required for the Northern Hemi image (1828x3643) is about 5 seconds on a regular workstations. • Full disk image about 15 seconds. • COUNS Image about 2 seconds. • The efficient meets the latency requirement in the SPS data processing. • Will be further evaluated in the SPS operational environment as a part of the functional test.

  11. The dependence of correlation on the mis-registration using FFTR

  12. Special Case: GOES13 channel 6 to channel 4 co-registration • The channel 6 for GOES13 has one detector, its north-south resolution is half of that for other channels. • The line in channel 6 is off by 0.5 pixel from the lines in other IR channels in North South direction. • The approach: scale channel 6 image so that it has the same resolution as the other IR channels. • Using the FFTR algorithm. is obtained through the image resampling algorithm FFTR from

  13. An example of Channel 6 image: the original image

  14. The scaled Channel 6 image

  15. The Histogram of the scaled and original images • The histogram for the scaled image is divided by 2 since its size is 2 times larger. • The two histograms agree with each other.

  16. The gradient histograms along North-South direction for the original and scaled image • The gradient for the original image is defined as g(x)=Pj-Pj-1 • The gradient for the scaled image is defined as g(x)= Pj+1-Pj-1 • The histogram for the scaled image is divided by 2. • The difference mainly comes from the small pixel value region. • The sharpness of the edges is maintained in the scaled image.

  17. The results: GOES14

  18. Result: GOES15

  19. Results: GOES13

  20. Result Summary • Larger EW mis-registration in 2-4 and 3-4 for GOES14. • 6-4 has been consistently smaller. • Have to perform the resampling for two channels • Either 2,3 or 4,6. • The magnitude of the mis-registration for GOES15 is small. • No resampling is needed. • Lager mis-registration in 2-4 for GOES13 in EW direction. • Larger mis-registration in 6-4 in NS direction with a lot of noises. • All data has a 24 hour periodic behavior. • Overall, mis-registration in 6-4 EW direction is small. • The resampling can be performed in channel 2, 3 or 4,6. • Should look at the landmark to see the mis-registration between IR and visible channels to determine which channels should be resampled.

  21. The co-registration table generation • Least Square Fit the data using the following formula • N=5 in this case, 11 parameters for more than 300 data points • the time t has the hour unit.

  22. The Visible-IR Channel 2 Co-registration and IR EW CORT Table: GOES13 data • The x axis is the number of the half hour increment. • The visible channel is the reference channel • Channel 2 pixel is on the west side of the visible pixel • Channel 2 pixel is on the west side of the channel 4 pixel as well.

  23. The Visible-IR Channel 2 Co-registration and IR EW CORT Table: GOES14 data

  24. The Visible-IR Channel 2 Co-registration and IR CORTTable: GOES15 data

  25. Summary of the resampling approach • Visible-IR2 CORT Table comes from Landmark evaluation from OATS. • The time axis is half hour increment for 24 hour period. • The Visible-IR2 uses Visible channel as reference. • The IR 2-4 co-registration uses channel as the reference. • This leads to the opposite sign in mis-registrations. • The resampling on channels 3, 4, and 6 would be best. • The overall value for GOES14 is small in EW direction for Vis-IR2 • For GOES13, the region where resampling is needed, the value for Vis-IR2 is small in EW direction. • Will not impact on OATS CORT Table generation. • No resampling is needed for GOES15. • The overall value is less than 0.4 pixels in magnitude.

  26. Resample example: Original Image • The image: GOES13 channel 4 at 2012/258/11:15.

  27. Resample Example: the resampled image • The image is shifted by 0.5 pixels

  28. The temperature difference between channel 2 and 4 Before resampling After resampling

  29. Implementation: OPS Concept RPM Archive coreg- retrieval Coreg Table SPS correction GVAR • RPM (Replace Production Monitor) Archive: • Contains 5 days of GVAR data. • Ingested from the realtime GVAR stream. • Used as the input for the IR co-registration evaluation. • coreg-retrieval: • Offline software package to use the GVAR data from RPM archive as the input. • Generates the IR-co-registration table • Only runs when needed, assume that the IR channels co-registraction is relatively stable. • The software package will be installed on CAWS Workstations. • The software is ready to be operational.

  30. OPS Concepts (Continue) • Coreg Table: • IR chan-chan mis-registration table. • An floating number array • Contains 48 elements, and each element corresponding to the mis-registration in every half hour during the 24 hour period. • The output of the coreg-retrieval software package. • The Co-Registration table will be loaded on the SPS database after the new table is generated. • Chan2-chan4 mis-registration value for the specific time will added to GVAR block 0. • SPS perform the resampling on channel 2,3 or 4,6 images to make it align with the images in other IR channels if the mis-registration is larger than certain values (0.5 pixel for example). • This step is optional depends on the user data validation of the channel 4 resampled images. • SPS will have the option to enable or disable the IR channel re-sampling if this is implemented.

  31. OPS Concept (Continue) • If the GVAR images are corrected for mis-registration in IR Channels. • The output of the coreg-retrieval software will be used to monitor the quality of coreg table in the SPS. • The mis-registration retrieved from co-registration corrected images should be very small. • Measuring the difference between the actual mis-registrations and the ones used in the correction. • If the mis-registrations from the co-registration corrected images are larger (>0.3 for example) • The new coreg-table will be created by the combining the coreg-table used in SPS and the output from the coreg-retrieval software. • New table will be loaded into SPS.

  32. Implementation in SPS • Add the IR co-registration tables in SPS database. • Add IR channel to channel mis-registration value to GVAR block 0. • The re-sampling process will performed after the IR calibration. • Adding the control parameter in SPS to enable/disable the resampling function. • Before the straylight correction. • The implementation of the re-sampling process depends on user’s evaluation of the re-sampled image. • Add the re-sampled status word in the GVAR block 0 to indicate if the IR channel images are re-sampled.

  33. Proposed GVAR Block0 Changes GVAR Block 0 with IR channel co-registration. Refer to Table 3-6 in GOES Interface Document 504-02 for complete information

  34. Current Status and Schedule • The Software package for the IR CORT Table generation is ready to be operational. • The resampling code has been migrated to c platform. • SPS implementation is ready to start. • The new version can be released early next year. (Feb-March Timeframe).

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