Cloud Imagery and Motion

1
Cloud Imagery and Motion

• Mark Anderson, Scott Cornelsen, and Tom Wilkerson
• Space Dynamics Laboratory
• Utah State University, Logan, UT 84341
• 435-797-4679 manderson_at_sdl.usu.edu,
• scornelsen_at_sdl.usu.edu, tdw_at_sdl.usu.edu
• Presentation for Working Group on Space-Based
  Lidar Winds
• Bar Harbor, ME June 23-25, 2003
• Research Support IPO, NASA, and SDL

2
MCP (Moving Cloud Patterns) Overview

• Incorporates a block matching technique to
  quantify cloud motion in pixels
• Camera lens distortion and field of view
  information used to convert from pixel to angular
  displacements
• Cloud height measurement needed for final
  velocity calculation

Improvements

• Image subtraction to eliminate background and sun
  glare
• MCP data filtering software
• Automatic contrail detection (to be incorporated
  in near future)

3
Algorithm Improvement Image Subtraction

• Absolute difference between frames, along with
  difference threshold, are used to create a binary
  mask
• Mask eliminates stationary objects (i.e. sun
  glare, etc.) from consideration during block
  matching
• Produces substantial improvements in cloud motion
  detection and measurement

4
MCP Output Variability

• Absence of visible clouds
• Low contrast of cloud features
• Multiple cloud layers visible during image
  sequence

5
Analysis of MCP Output

• Time Based Filtration
• Distribution Based Filtration
• Velocity Distribution
• Direction Distribution

6
MCP Analysis Program

• Simultaneously filter on three parameters
• Automatic calculation of velocity and direction
• File format preserves filter settings and user
  input
• Plots exportable in all standard image formats
• Exports final results in text file format
• Increased efficiency and versatility of analysis

7
Utility of Contrail Analysis

• Automatic detection of contrails to avoid
  improper analysis in sky-scanning observation.
• Determination of the number of contrails present.
• Identification of contrail location.

8
Hough Transform picks out each straight line in
the image as an intersection of curves in ?,?
space
r
each line-point generates a Hough curve ?(?)
line to be identified (m,b)
q
p
p/2
Image Space
Transform Space
y(x) mx b becomes ?(?) x cos? y
sin?
9
Contrail Detection Sequence
Original Image
Edges Detected
Composite Image
3
1
4
5
3
6
2
4
1
5
2
6
10
Summary

• Notable improvement in quality of MCP
  measurements due to image subtraction algorithms
• Efficiency and versatility of MCP output analysis
  improved with new software
• Contrail detection identifies possible times of
  inaccurate analysis by other instruments