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Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data. James C. Tilton Mail Code 606* NASA GSFC Greenbelt, MD 20771 James.C.Tilton@nasa.gov. William T. Lawrence Natural Sciences Bowie State University Bowie, MD 20715 wlawrence@bowiestate.edu.

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Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

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  1. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606* NASA GSFCGreenbelt, MD 20771 James.C.Tilton@nasa.gov William T. Lawrence Natural Sciences Bowie State University Bowie, MD 20715 wlawrence@bowiestate.edu *Computational & Information Sciences and Technology Office MultTemp 2005, Biloxi, MS

  2. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Proposal: To develop tools and methods for automated change detection from remotely sensed imagery utilizing a previously developed approach for creating segmentation hierarchies from imagery data. Step-1 Proposal has been submitted to the ROSES-2005 NRA, Land-Cover/Land-Use Change Element MultTemp 2005, Biloxi, MS

  3. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data What is a Segmentation Hierarchy? • A set of image segmentations that • consist of segmentations at different levels of detail, in which • the coarser segmentations can be produced from merges of regions from the finer segmentations, and • the region boundaries are maintained at the full image spatial resolution. MultTemp 2005, Biloxi, MS

  4. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Advantages of a Segmentation Hierarchy • Image Analysis is transformed from pixel-by-pixel analysis into object-by-object analysis, allowing the utilization of object shape, texture and context for a more robust and accurate analysis. • A hierarchy of segmentations allows dynamic selection of the appropriate level of segmentation detail for each object of interest. MultTemp 2005, Biloxi, MS

  5. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Example: Ikonos Data • Collected May 17, 2000 over Baltimore, MD. • Four meter spatial resolution. • Four spectral bands: blue, green, red and nir. • 384x384 pixel sub-section. • Twelve-level hierarchical segmentation. MultTemp 2005, Biloxi, MS

  6. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Original Image MultTemp 2005, Biloxi, MS

  7. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 30 Regions MultTemp 2005, Biloxi, MS

  8. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 18 Regions MultTemp 2005, Biloxi, MS

  9. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 11 Regions MultTemp 2005, Biloxi, MS

  10. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 8 Regions MultTemp 2005, Biloxi, MS

  11. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 6 Regions MultTemp 2005, Biloxi, MS

  12. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 4 Regions MultTemp 2005, Biloxi, MS

  13. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Original Image MultTemp 2005, Biloxi, MS

  14. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Twelve Level Hierarchical Boundaries MultTemp 2005, Biloxi, MS

  15. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data RHSEG and HSEGViewer Hierarchical Segmentations produced by RHSEG: • RHSEG is a hybrid of Hierarchical Step-Wise Optimization* region growing with spectral clustering – controlled by spclust_wght parameter. • * J. M. Beaulieu and M. Goldberg, “Hierarchy in picture • segmentation: A stepwise optimal approach,” • IEEE Transactions on Pattern Analysis and Machine • Intelligence, vol. 11, no. 2, pp. 150-163, 1989. MultTemp 2005, Biloxi, MS

  16. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data RHSEG and HSEGViewer • Recursive implementation facilitates a highly efficient parallel implementation – a full Landsat TM scene (6500x6500 by 6 bands) can be processed in under 10 minutes with 256 CPUs. • The HSEGViewer program provides a convenient, user-friendly, tool for visualizing and interacting with the image segmentation hierarchies produced by the RHSEG program. MultTemp 2005, Biloxi, MS

  17. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data RHSEG and HSEGViewer • HSEGViewer and demo version of RHSEG are available through http://tco.gsfc.nasa.gov/RHSEG/index.html • More information on RHSEG available at http://cisto.gsfc.nasa.gov/TILTON/index.html MultTemp 2005, Biloxi, MS

  18. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Monitoring Change: First Steps Assembled a multi-season, multi-year test data set from MODIS Terra AM1 platform for initial tests: • Bands 1-7 (aggregated to 1KM) • Twelve dates: 31 JAN 2003, 19 APR 2003, 09 AUG 2003, 21 OCT 2003, 28 OCT 2003, 18 NOV 2003, 01 FEB 2004, 20 MAR 2004, 11 JUN 2004, 24 SEP 2004, 29 NOV 2004, 28 FEB 2005. • 1002x1002 pixels at 1km spatial resolution centered roughly over the Salton Sea. • Southern California fires visible in 28 OCT 2003 scene. MultTemp 2005, Biloxi, MS

  19. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 21 OCT 2003 Bands 7, 2 & 1 Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  20. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 28 OCT 2003 Bands 7, 2 & 1 Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  21. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Bands 7, 2 & 1 Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  22. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Hierarchical Boundary Map 15 regions MultTemp 2005, Biloxi, MS

  23. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Hierarchical Boundary Map 9 regions MultTemp 2005, Biloxi, MS

  24. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Bands 7, 2 & 1 Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  25. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data * Normalized Dissimilarity vs. Region Mean at Finest Hierarchical Level. MultTemp 2005, Biloxi, MS

  26. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Sum of Masks from Minimum Mean Regions Histogram 0: 644777 1: 2211 2: 1041 3: 872 4: 1127 5: 4703 6: 20366 7: 63794 8: 109386 9: 87193 10: 57075 11: 26905 12: 29126 MultTemp 2005, Biloxi, MS

  27. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Land vs. Water Mask (0-3 designated as land and 4-12 as water.) MultTemp 2005, Biloxi, MS

  28. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Cloud and Snow Detection/Masking 31 JAN 2003 Data Set (A2003031.1815) Brightest Region: Region 22 is clearly a cloud at all hierarchical levels (by inspection) MultTemp 2005, Biloxi, MS

  29. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  30. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud Region (water masked) MultTemp 2005, Biloxi, MS

  31. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Cloud and Snow Detection/Masking 31 JAN 2003 Data Set (A2003031.1815) Selected Region: Region 51//55 is mountain snow at hierarchical levels 0 through 33 (by inspection – and change in normalized dissimilarity) MultTemp 2005, Biloxi, MS

  32. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  33. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked) MultTemp 2005, Biloxi, MS

  34. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Cloud and Snow Detection/Masking 31 JAN 2003 Data Set (A2003031.1815) Selected Region: Region 64 is mountain snow through hierarchical level 4 (by inspection – and change in normalized dissimilarity) MultTemp 2005, Biloxi, MS

  35. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  36. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked) MultTemp 2005, Biloxi, MS

  37. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Cloud and Snow Detection/Masking 31 JAN 2003 Data Set (A2003031.1815) Selected Region: Region 61 is mountain snow through hierarchical level 22 (by inspection – and change in normalized dissimilarity) MultTemp 2005, Biloxi, MS

  38. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  39. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked) MultTemp 2005, Biloxi, MS

  40. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water, clouds & snow masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  41. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Bands 7, 2 & 1 (water masked) Band: bandwidth 1: 620- 670nm 2: 841- 875nm 7: 2105-2155nm MultTemp 2005, Biloxi, MS

  42. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Monitoring Change: Next Steps • Obtain Cloud Mask data product (MOD 35) for pertinent data set dates and compare with RHSEG results. • If available, obtain Snow Cover data product (MOD 10) and compare with RHSEG results. • Obtain other pertinent MODIS data products (e.g. MOD 12 – Land Cover/Land Cover Change, MOD 14 – Thermal Anomalies, Fires & Biomass Burning, MOD 13 – Gridded Vegetation Indices, …) for analysis and comparison. MultTemp 2005, Biloxi, MS

  43. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Monitoring Change: Next Steps • Develop more flexible tools for analyzing RHSEG segmentation hierarchies, including improvements to HSEGViewer. • Implement other dissimilarity criteria in RHSEG, such as the “Spectral Angle Mapper” criterion. • Implement tools to evaluate various spatial features for use in analyzing the RHSEG segmentation hierarchies, such as convex_area, solidity, and extent, as well as texture and fractal measures. • Implement tools to find and track corresponding regions across multi-temporal data sets. MultTemp 2005, Biloxi, MS

  44. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Monitoring Change: Future Plans • Automate process to flag areas with intra-data change • Create a rule-based automated classification system to label regions • Create a system to evaluate change as “expected” or “unexpected” • Use a rules-based system to flag areas of change that are not expected Automated evaluation of change would facilitate (human) follow-up for change mediation/intervention MultTemp 2005, Biloxi, MS

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