Presentation is loading. Please wait.

Presentation is loading. Please wait.

May 16-18, 2005MultTemp 2005, Biloxi, MS1 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606*

Published byLee Harris Modified over 8 years ago

Similar presentations

Presentation on theme: "May 16-18, 2005MultTemp 2005, Biloxi, MS1 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606*"— Presentation transcript:

1 May 16-18, 2005MultTemp 2005, Biloxi, MS1 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 *Computational & Information Sciences and Technology Office

2 May 16-18, 2005MultTemp 2005, Biloxi, MS2 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

3 May 16-18, 2005MultTemp 2005, Biloxi, MS3 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data A set of image segmentations that i.consist of segmentations at different levels of detail, in which ii.the coarser segmentations can be produced from merges of regions from the finer segmentations, and iii.the region boundaries are maintained at the full image spatial resolution. What is a Segmentation Hierarchy?

4 May 16-18, 2005MultTemp 2005, Biloxi, MS4 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  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. Advantages of a Segmentation Hierarchy

5 May 16-18, 2005MultTemp 2005, Biloxi, MS5 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image 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. Example: Ikonos Data

6 May 16-18, 2005MultTemp 2005, Biloxi, MS6 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Original Image

7 May 16-18, 2005MultTemp 2005, Biloxi, MS7 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 30 Regions

8 May 16-18, 2005MultTemp 2005, Biloxi, MS8 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 18 Regions

9 May 16-18, 2005MultTemp 2005, Biloxi, MS9 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 11 Regions

10 May 16-18, 2005MultTemp 2005, Biloxi, MS10 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 8 Regions

11 May 16-18, 2005MultTemp 2005, Biloxi, MS11 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 6 Regions

12 May 16-18, 2005MultTemp 2005, Biloxi, MS12 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Mean Image with 4 Regions

13 May 16-18, 2005MultTemp 2005, Biloxi, MS13 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Original Image

14 May 16-18, 2005MultTemp 2005, Biloxi, MS14 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Twelve Level Hierarchical Boundaries

15 May 16-18, 2005MultTemp 2005, Biloxi, MS15 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 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. RHSEG and HSEGViewer

16 May 16-18, 2005MultTemp 2005, Biloxi, MS16 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  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. RHSEG and HSEGViewer

17 May 16-18, 2005MultTemp 2005, Biloxi, MS17 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  HSEGViewer and demo version of RHSEG are available through http://tco.gsfc.nasa.gov/RHSEG/index.html http://tco.gsfc.nasa.gov/RHSEG/index.html  More information on RHSEG available at http://cisto.gsfc.nasa.gov/TILTON/index.html http://cisto.gsfc.nasa.gov/TILTON/index.html RHSEG and HSEGViewer

18 May 16-18, 2005MultTemp 2005, Biloxi, MS18 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 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. Monitoring Change: First Steps

19 May 16-18, 2005MultTemp 2005, Biloxi, MS19 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

20 May 16-18, 2005MultTemp 2005, Biloxi, MS20 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

21 May 16-18, 2005MultTemp 2005, Biloxi, MS21 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

22 May 16-18, 2005MultTemp 2005, Biloxi, MS22 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Hierarchical Boundary Map 15 regions

23 May 16-18, 2005MultTemp 2005, Biloxi, MS23 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 01 FEB 2004 Hierarchical Boundary Map 9 regions

24 May 16-18, 2005MultTemp 2005, Biloxi, MS24 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

25 May 16-18, 2005MultTemp 2005, Biloxi, MS25 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Data setRegion Label # of Pixels Crit. Value N. Dissim.* vs. HL 0 Hierarchical Levels 31 JAN 200313727492540.24894332-35 19 APR 200312852612320.20437733-36 09 AUG 200313318342120.067451928-31 21 OCT 20031229176420.008938113-33 28 OCT 200313985222320.44000728-29 18 NOV 20031322248680.002150717-30 01 FEB 200413026151770.11427332-39 20 MAR 20041868747105910.487936 20 MAR 200462634344982.3626735 20 MAR 2004781990360.0176414-34 11 JUN 200433556355302.8638432-33 11 JUN 200441165371080.17943723-31 24 SEP 20041361295800.046159922-30 29 NOV 20041387895840.006117623-36 28 FEB 200512896061610.16038530-35 * Normalized Dissimilarity vs. Region Mean at Finest Hierarchical Level.

26 May 16-18, 2005MultTemp 2005, Biloxi, MS26 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

27 May 16-18, 2005MultTemp 2005, Biloxi, MS27 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Land vs. Water Mask (0-3 designated as land and 4-12 as water.)

28 May 16-18, 2005MultTemp 2005, Biloxi, MS28 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data Region Label # of PixelsCrit. Value N. Dissim. vs. HL 0 Boundary Npix/Area Hierarchical Levels 22239331871.2040.05131-37 2219723910.2940.05823-30 2211565290.00.1600-22 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)

29 May 16-18, 2005MultTemp 2005, Biloxi, MS29 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

30 May 16-18, 2005MultTemp 2005, Biloxi, MS30 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud Region (water masked)

31 May 16-18, 2005MultTemp 2005, Biloxi, MS31 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) Region Label # of Pixels Crit. Value N. Dissim. vs. HL 0 Boundary Npix/Area Hierarchical Levels 158039686642.930.01437 123397434044.170.26836 158333327234.750.09834-35 51305553.11.1530.30513-33 5513580.0 0.6020-12

32 May 16-18, 2005MultTemp 2005, Biloxi, MS32 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

33 May 16-18, 2005MultTemp 2005, Biloxi, MS33 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked)

34 May 16-18, 2005MultTemp 2005, Biloxi, MS34 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) Region Label # of Pixels Crit. Value N. Dissim. vs. HL 0 Boundary Npix/Area Hierarchica l Levels 158039686626.840.01437 319393516527.670.48229-36 68113212023.440.37927-28 133523750.222.020.64512-26 50190435.612.830.4635-11 64940.0 0.6280-4

35 May 16-18, 2005MultTemp 2005, Biloxi, MS35 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

36 May 16-18, 2005MultTemp 2005, Biloxi, MS36 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked)

37 May 16-18, 2005MultTemp 2005, Biloxi, MS37 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) Region Label # of Pixels Crit. Value N. Dissim. vs. HL 0 Boundary Npix/Area Hierarchica l Levels 158039686611.440.01437 12339743418.5100.26836 115064121410.910.37633-35 87335412411.320.41528-32 142752791.511.190.37423-27 6171321.50.00.5500-22

38 May 16-18, 2005MultTemp 2005, Biloxi, MS38 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

39 May 16-18, 2005MultTemp 2005, Biloxi, MS39 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data 31 JAN 2003 Cloud and Snow Regions (water masked)

40 May 16-18, 2005MultTemp 2005, Biloxi, MS40 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

41 May 16-18, 2005MultTemp 2005, Biloxi, MS41 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

42 May 16-18, 2005MultTemp 2005, Biloxi, MS42 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  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. Monitoring Change: Next Steps

43 May 16-18, 2005MultTemp 2005, Biloxi, MS43 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  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. Monitoring Change: Next Steps

44 May 16-18, 2005MultTemp 2005, Biloxi, MS44 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data  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 Monitoring Change: Future Plans

Download ppt "May 16-18, 2005MultTemp 2005, Biloxi, MS1 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606*"

Similar presentations

MODIS The MODerate-resolution Imaging Spectroradiometer (MODIS ) Kirsten de Beurs.

MODIS The MODerate-resolution Imaging Spectroradiometer (MODIS ) Kirsten de Beurs.
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,

Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma.

Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma.
Incorporating regional knowledge into global data sets: Some ideas for rice in Asia Andy Nelson Thursday11 th Sep, 2014.

Incorporating regional knowledge into global data sets: Some ideas for rice in Asia Andy Nelson Thursday11 th Sep, 2014.
Change Detection. Digital Change Detection Biophysical materials and human-made features are dynamic, changing rapidly. It is believed that land-use/land-cover.

Change Detection. Digital Change Detection Biophysical materials and human-made features are dynamic, changing rapidly. It is believed that land-use/land-cover.
Line Efficiency     Percentage Month Today’s Date

Line Efficiency     Percentage Month Today’s Date
Texture Segmentation Based on Voting of Blocks, Bayesian Flooding and Region Merging C. Panagiotakis (1), I. Grinias (2) and G. Tziritas (3) 07-07-2009.

Texture Segmentation Based on Voting of Blocks, Bayesian Flooding and Region Merging C. Panagiotakis (1), I. Grinias (2) and G. Tziritas (3)
ASTER image – one of the fastest changing places in the U.S. Where??

ASTER image – one of the fastest changing places in the U.S. Where??
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Digital Imaging and Remote Sensing Laboratory Real-World Stepwise Spectral Unmixing Daniel Newland Dr. John Schott Digital Imaging and Remote Sensing Laboratory.

Digital Imaging and Remote Sensing Laboratory Real-World Stepwise Spectral Unmixing Daniel Newland Dr. John Schott Digital Imaging and Remote Sensing Laboratory.
CHANGE DETECTION METHODS IN THE BOUNDARY WATERS CANOE AREA Thomas Juntunen.

CHANGE DETECTION METHODS IN THE BOUNDARY WATERS CANOE AREA Thomas Juntunen.
Introduction to Digital Data and Imagery

Introduction to Digital Data and Imagery
Remote Sensing 2012 SUMMER INSTITUTE. Presented by: Mark A. Van Hecke National Science Olympiad Earth-Space Science Event Chair Roy Highberg North Carolina.

Remote Sensing 2012 SUMMER INSTITUTE. Presented by: Mark A. Van Hecke National Science Olympiad Earth-Space Science Event Chair Roy Highberg North Carolina.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.

Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –

Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center January 30, 2015 update National.

James C. Tilton Code Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center January 30, 2015 update National.
Use of Remote Sensing Data for Delineation of Wildland Fire Effects

Use of Remote Sensing Data for Delineation of Wildland Fire Effects
Corrie Hannah Mentor: Dr. Stuart E. Marsh, Office of Arid Lands Studies NASA Space Grant Symposium April 17, 2009 Arizona State University Using Remote.

Corrie Hannah Mentor: Dr. Stuart E. Marsh, Office of Arid Lands Studies NASA Space Grant Symposium April 17, 2009 Arizona State University Using Remote.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.

Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.

MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.

Similar presentations

Ads by Google