Star opdb wpop report
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STAR/OPDB WPOP Report. Jaime Daniels Wayne Bresky Nov 3, 2005. Topics. Feature Tracking: An investigation of an Optical Flow Approach Implementation of Error Estimation (EE) Technique Other Activities Planned for FY06 GOES-N readiness MTSAT readiness MODIS Winds Upgrades.

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STAR/OPDB WPOP Report

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Star opdb wpop report

STAR/OPDB WPOP Report

Jaime Daniels

Wayne Bresky

Nov 3, 2005


Topics

Topics

  • Feature Tracking: An investigation of an Optical Flow Approach

  • Implementation of Error Estimation (EE) Technique

  • Other Activities Planned for FY06

    • GOES-N readiness

    • MTSAT readiness

    • MODIS Winds Upgrades


Feature tracking an investigation of an optical flow approach

Feature Tracking: An investigation of an Optical Flow Approach

  • Motivation

    • Recommended by the Working Group on methods at the last two International Winds Workshops (IWW6, IWW&) for further investigation & development

      • Suggested goal: Compare performance against standard correlation techniques used

  • Presentation made at the last IWW7

    • “Motion Vectors in Weather Radar Images”

      • M. Peura and H. Hohti (FMI)

      • Optical flow algorithm adopted for this problem


Feature tracking an investigation of an optical flow approach1

Feature Tracking: An investigation of an Optical Flow Approach

  • Optical Flow is a commonly used technique in the vast computer vision field

    • Numerous optical flow approaches/algorithms are documented in the literature

    • Barron, J., D. Fleet, and S. Beauchemin, 1994: Systems and Experiments: Performance of Optical Flow Techniques. International Journal of Computer Vision, 12:1, 43-77.

  • General Definition: Optical flow is an approximation of the local image motion based upon local spatio-temporal derivatives of image intensity in a sequence of images

  • Underlying Assumption: Local changes in image intensity are explained only by motion


Feature tracking an investigation of an optical flow approach2

Feature Tracking: An investigation of an Optical Flow Approach

  • Current status

    • Optical flow algorithm selected for testing

      • Lucas, B., and Kanade, 1981: An iterative registration technique with an application to stereo vision. Proc. Int. Joint Conf on Artificial Intelligence, Aug24-28, Vancouver, British Columbia, 674-679

    • Software developed for above algorithm and integrated within winds vector calculation software used here at NESDIS

    • Running very controlled experiments using “simulated” imagery to validate/measure algorithm performance

      • Primarily using GOES-11 5-min imagery

        • Visible, WV, and LWIR imagery


Star opdb wpop report

Preliminary Results

Figure 4. GOES-11 visible cloud-drift winds (not quality controlled) generated from 5-min imagery using the standard correlation matching (control) and optical flow (test) algorithms.


Star opdb wpop report

Preliminary Results

Table 2. Comparison statistics between collocated GOES-11 raw water vapor winds (all levels) generated using correlation matching and optical flow tracking and rawinsondes at 00Z on August 3, 2005.


Preliminary findings

Preliminary Findings

  • Correlation-based tracking algorithm works very well when tracking motion of a field of small cumulus clouds; optical flow method struggles more in this situation

  • Optical flow algorithm seemed to perform quite well with WV imagery

  • However…., significant slow speed bias is being observed which needs to be addressed if this method is become a viable method of tracking

    • Actively being worked by Wayne


Implementation of error estimation ee technique

Implementation of Error Estimation (EE) Technique

  • Goal:Integrate EE software within operational winds software

  • Work has started on this integration effort

    • Coordination with Chris Redder and John LeMarshall

    • Chris has supplied us with the EE software

    • Wayne is currently integrating the software

    • Real-time generation of EE by end of next week planned


Other activities planned for fy06

Other Activities Planned for FY06

  • GOES-N Readiness

    • Obtained latest transmittance coefficient files

    • Very few software mods required

    • GOES-N science test participation

      • Assess impacts of expected improvements to calibration, navigation/registration

    • G-PSDI proposal submitted

  • MTSAT Readiness

    • CIMSS developed capability with OSD/GS funding

    • G-PSDI proposal submitted to integrate MTSAT capability within operational winds code

  • MODIS Winds Upgrades (FY06 P-PSDI Proposal approved)

    • Coordinate with CIMSS group to integrate planned upgrades

      • Parallax correction

      • Mixed Terra & Aqua winds processing

      • Validation of


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