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Irina Gladkova 1,2,3 , Yury Kihai 1 ,2 , Alexander Ignatov 1 ,

Irina Gladkova 1,2,3 , Yury Kihai 1 ,2 , Alexander Ignatov 1 , Fazlul Shahriar 3,4 , Boris Petrenko 1,2 1 NOAA/NESDIS/STAR, 2 GST, Inc., 3 City College of New York, NOAA/CREST, 4 Graduate Center of CUNY. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014.

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Irina Gladkova 1,2,3 , Yury Kihai 1 ,2 , Alexander Ignatov 1 ,

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  1. Irina Gladkova1,2,3, Yury Kihai1,2, Alexander Ignatov1, Fazlul Shahriar3,4, Boris Petrenko1,2 1NOAA/NESDIS/STAR, 2GST, Inc., 3City College of New York, NOAA/CREST, 4 Graduate Center of CUNY. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014 Exploring Pattern Recognition Enhancements to ACSPOClear-Sky Mask for VIIRS: Potential and Limitations.

  2. Motivation • ACSPO Clear-Sky Mask (ACMS) employs comparisons of retrieved SST with L4 analyses, reflectance threshold tests and spatial uniformity tests. • ACSM performs well on a global scale but tends to over-screen some highly dynamic areas (e.g., with strong currents, cold upwellings, eddies) as well as the coastal zones. • These deficiencies cannot be completely eliminated by simple thresholds adjustment within ACSM without triggering massive cloud leakages. • Visual analysis of SST field easily discriminates cloud leakages from cold SST anomalies SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  3. VIIRS SST SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  4. ACSPO Clear Sky Mask SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  5. Types of misclassifications • Typical clear sky ocean regions misclassified by the ACSM : • contiguous, • with well-defined boundaries, • typically located in the vicinity of ocean thermal fronts. • Existing image processing techniques: • Segmentation; • Morphological Procedures: erosion and dilation; • Thermal Front Detection. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  6. Human Perspective • Humaneye does not perceive absolute pixel values (i.e., SST values) • It relies instead on local contrasts and ratios, which more directly correlate with gradients in an image. • Difference between ocean and cloud patterns should be more pronounced in the SST gradient magnitude domain. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  7. Magnitude Gradient Gradient magnitudes viewed as a terrain look like sharp ridges towering over flat valleys. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  8. Algorithm Step 1: Identify Search Domain Step 2: Determine SST gradient ridges Step 3:Determine spatially connected cold SST regions Step 4: Discard SST segments found in Step 3 that do not border the ridges found in Step 2 Step 5: Statistical Test SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  9. Search Space SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  10. Gradient Ridges SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  11. Segmentation SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  12. Adjacency SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  13. Statistical Test SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  14. Clear Sky Regions SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  15. SST Gradient Ridges • Existing Image Processing Tools: • Thermal Front Detection • Edge Detection • Gradient Ridges and Valleys SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  16. Border Stability SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  17. SST Gradient Ridges SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  18. Watershed Segmentation • Segmentation/Clustering is a well studied field • Many ways to perform segmentation • We use watershed type applied to ΔSST SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  19. Steps of Segmentation Segments obtained via iterative procedure: Iter0:Initial segments Iter k:Lower the threshold level Find new “catchment basins” Re-label in case of split SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  20. Practical Considerations • Bow-Ties • Striping • Missing Reference SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  21. Potentials and Limitations • Considered 2 sets of VIIRS data: • 48 hand picked and cropped regions with typical clear sky misclassification • 144 granules representing 1 day global observations Results were visually inspected and analyzed; Success rate is promising but more work is needed. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  22. South Africa, 02/17/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  23. South Africa, 02/17/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  24. Gulf Stream, 05/10/13 (day) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  25. Gulf Stream, 05/10/13 (day) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  26. Uruguay, 05/05/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  27. Uruguay, 05/05/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  28. Pamlico Sound, 02/16/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  29. Pamlico Sound, 02/16/13 (night) SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

  30. Conclusion • A supplemental algorithm to the current ACSPO Clear-Sky Mask based on pattern recognition is being explored. • Our preliminary analyses suggest that some of the limitations inherent to the current ACSM may be alleviatedand SST coverage improved. • The improvements are mostly noticeable in the areas interesting to ACSPO users, including dynamic areas of the ocean and coastal zones. • Future work will include tuning the algorithm, with emphasis on resolving the remaining cloud leakages. SPIE, Ocean Sensing and Monitoring VI, Baltimore, 5-9 May 2014

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