The Pennsylvania State University The Graduate School College of Engineering IMPROVING THE DETECTION OF WIND FEATURES IN BACKSCATTER LIDAR SCANS USING FEATURE EXTRACTION A Thesis in Electrical Engineering by Eric S. Rotthoff c 2012 Eric S. Rotthoff Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science December 2012 ii The thesis of Eric S. Rotthoff was reviewed and approved∗ by the following: Timothy J. Kane Professor of Electrical Engineering Thesis Adviser Julio Urbina Associate Professor of Electrical Engineering Kultegin Aydin Professor of Electrical Engineering Head of the Department of Electrical Engineering ∗Signatures are on file in the Graduate School. iii Abstract This thesis presents the results of applying image segmentation techniques to incoherent LIDAR data to improve the detection of wind features. Improving the de- tection 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 segmen- tation 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 pro- vides 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. iv Table of Contents List of Tables :::::::::::::::::::::::::::::::::::::: vi List of Figures ::::::::::::::::::::::::::::::::::::: vii Acknowledgments ::::::::::::::::::::::::::::::::::: x Chapter 1. Introduction :::::::::::::::::::::::::::::::: 1 1.1 Background . 1 1.2 Motivation . 1 1.3 REAL LIDAR . 4 1.4 NEXRAD Radar . 5 Chapter 2. Data Format and Conditioning ::::::::::::::::::::: 7 2.1 Running Median High Pass Filter . 8 2.2 Cartesian Mapping with Linear Interpolation . 9 2.3 Temporal-Mean Image Creation . 13 Chapter 3. Segmentation ::::::::::::::::::::::::::::::: 14 3.1 Cloud Segmentation . 14 3.2 Segmentation Results . 17 Chapter 4. Results ::::::::::::::::::::::::::::::::::: 19 4.1 LIDAR Results . 19 4.1.1 Neighboring Clouds . 20 4.1.2 Tracking A Feature . 31 4.1.3 Errant Results . 41 4.1.4 Autocorrelation . 48 4.1.5 Comparison . 57 4.2 NEXRAD Results . 59 4.2.1 Example 1 . 59 4.2.2 Autocorrelation . 75 4.2.3 Comparison . 81 Chapter 5. Conclusions :::::::::::::::::::::::::::::::: 82 5.1 Summary . 82 5.2 Future Work . 83 Appendix A. REAL LIDAR Data Extraction :::::::::::::::::::: 85 v Appendix B. Code Description :::::::::::::::::::::::::::: 87 B.1 read translate.py ........................... 87 B.2 avg filt make movie.py ........................ 88 B.3 segment image.py ............................ 88 B.4 analyze log.py ............................. 90 References :::::::::::::::::::::::::::::::::::::::: 91 vi List of Tables 4.1 Peak Shape Moments for Correlations of REAL Scan Features (N=291) 58 4.2 Peak Shape Moments for Correlations of NEXRAD Features (N=128) . 81 A.1 Record Specification for REAL's Data . 86 vii List of Figures 1.1 Raman-shifted Eye-safe Aerosol LIDAR (REAL) in Operation . 5 1.2 WSR-88D KCCX Located in State College, Pennsylvania . 6 2.1 Bilinear interpolation example. 11 2.2 Bilinear interpolation in Radial Coordinate System. 12 3.1 Contour Output . 17 3.2 Contour Output Detail . 18 3.3 Feature in Frame . 18 3.4 Feature Detail . 18 4.1 Cloud 1 Frame 1 . 21 4.2 Cloud 1 Isolated . 22 4.3 Cloud 1 Search Space . 22 4.4 Cloud 1 Raw Correlation . 23 4.5 Cloud 1 Filtered Correlation . 24 4.6 Cloud 1 Motion Estimate . 24 4.7 Cloud 2 Frame 1 . 25 4.8 Cloud 2 Isolated . 25 4.9 Cloud 2 Search Space . 26 4.10 Cloud 2 Raw Correlation . 26 4.11 Cloud 2 Filtered Correlation . 27 4.12 Cloud 2 Motion Estimate . 27 4.13 Cloud 3 Frame 1 . 28 4.14 Cloud 3 Isolated . 28 4.15 Cloud 3 Search Space . 29 4.16 Cloud 3 Raw Correlation . 29 4.17 Cloud 3 Filtered Correlation . 30 4.18 Cloud 3 Motion Estimate . 30 4.19 Cloud in Frame 1 . 31 4.20 Cloud Isolated in Frame 1 . 32 4.21 Cloud Search Space in Frame 2 . 32 4.22 Cloud Raw Correlation Frame 1 . 33 4.23 Cloud Filtered Correlation Frame 1 . 34 4.24 Cloud Motion Estimate From Frame 1 to Frame 2 . 34 4.25 Cloud in Frame 2 . 35 4.26 Cloud Isolated in Frame 2 . 35 4.27 Cloud Search Space in Frame 3 . 36 4.28 Cloud Raw Correlation Frame 2 . 36 4.29 Cloud Filtered Correlation Frame 2 . 37 4.30 Cloud Motion Estimate From Frame 2 to Frame 3 . 37 4.31 Cloud in Frame 3 . 38 viii 4.32 Cloud Isolated in Frame 3 . 38 4.33 Cloud Search Space in Frame 4 . 39 4.34 Cloud Raw Correlation Frame 3 . 39 4.35 Cloud Filtered Correlation Frame 3 . 40 4.36 Cloud Motion Estimate From Frame 3 to Frame 4 . 40 4.37 Cloud 2 Frame 1 . 42 4.38 Cloud 2 Isolated . 42 4.39 Cloud 2 Search Space . 43 4.40 Cloud 2 Raw Correlation . 43 4.41 Cloud 2 Filtered Correlation . 44 4.42 Cloud 2 Motion Estimate . 44 4.43 Cloud in Error 2 . 45 4.44 Cloud Isolated in Error 2 . 45 4.45 Cloud Search Space in Error 2 . 45 4.46 Cloud Raw Correlation Error 2 . 46 4.47 Cloud Filtered Correlation Error 2 . 47 4.48 Cloud Motion Estimate Error 2 . 47 4.49 Cloud 1 Frame 1 . 48 4.50 Segmented Autocorrelation of Cloud 1 . 49 4.51 Raw Autocorrelation of Cloud 1 . 50 4.52 Cloud 2 Frame 1 . 51 4.53 Segmented Autocorrelation of Cloud 2 . 52 4.54 Raw Autocorrelation of Cloud 2 . 53 4.55 Cloud 3 Frame 1 . 54 4.56 Segmented Autocorrelation of Cloud 3 . 55 4.57 Raw Autocorrelation of Cloud 3 . 56 4.58 Radar Cloud 1 . 61 4.59 Radar Cloud 1 Isolated . 61 4.60 Radar Cloud 1 Search Space . 62 4.61 Radar Cloud 1 Correlation . 63 4.62 Radar Cloud 1 Motion . 64 4.63 Radar Cloud 2 . 65 4.64 Radar Cloud 2 Isolated . 65 4.65 Radar Cloud 2 Search Space . 66 4.66 Radar Cloud 2 Correlation . 67 4.67 Radar Cloud 2 Motion . 68 4.68 Radar Cloud 3 . 69 4.69 Radar Cloud 3 Isolated . 69 4.70 Radar Cloud 3 Search Space . 70 4.71 Radar Cloud 3 Correlation . 71 4.72 Radar Cloud 3 Motion . 72 4.73 NEXRAD Radial Velocity Measurement of Cloud 1 . ..