Improving the Detection of Wind Features In Backscatter LIDAR Scans Using Feature Extraction

Open Access
Rotthoff, Eric Stephen
Graduate Program:
Electrical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 12, 2012
Committee Members:
  • Timothy Joseph Kane, Thesis Advisor
  • wind
  • scanning LIDAR
  • image segmentation
  • wind field
  • feature extraction
This thesis presents the results of applying image segmentation techniques to incoherent LIDAR data to improve the detection of wind features. Improving the detection and analysis of wind information from incoherent LIDAR systems will allow for the adoption of these relatively low cost instruments in environments where the size, complexity, and cost of other options is prohibitive. By applying filtering and segmentation techniques to major features in each scan the detection and isolation of trackable features was accomplished. The same process was applied to NEXRAD reflectivity data to confirm the process described is instrument agnostic. The NEXRAD data also provides an estimate of radial particle motion allowing for a comparison with independent measurements. These techniques continue the development of a robust and accurate method of wind estimation using non-coherent LIDAR systems.