本研究使用影像處理技術發展雨滴譜儀。此系統使用攝影機擷取降落的雨滴，並且使用影像處理技術偵測分析雨滴特性諸如雨滴形狀與降落速度。藉由觀測出的資料可以用來偵測降雨型態以及增進小範圍降雨分析的準確度。在此系統中，使用一遠心鏡頭與攝影機搭配可以減少因失焦造成的模糊與變形現象進而增進量測雨滴的準確度。然而，使用遠心鏡頭必須提供相當足量的光照；因此，我們增設一個光源以補足亮度。由於攝影機的幀幅限制，無法於單次量測完整偵測並分析所有參數特性。正因如此，此系統使用兩種長短曝光時間來分別偵測雨滴形狀與雨滴速度。再者，此系統的影像處理步驟也會於本研究詳細介紹。
最後，我們採取三種不同實驗以驗證此系統︰玻璃珠實驗驗證了此系統架構與處理步驟之可行性；灑水實驗模擬了玻璃珠實驗所不及的較複雜與充滿不確定因素的降雨情形。最後的降雨實驗則是拍攝並分析實際降雨情形，而後將計算出的資料與中央氣象局新竹氣象站之統計資料進行比較。在降雨量的估測我們獲得了14.42%的誤差。

In this sturdy, an image-based disdrometer has been developed. The system uses a CCD camera to capture falling raindrops and applies proper image processing techniques to determine the characteristics of raindrops. The observed data can be used to analyze raining condition and increase the accuracy of analyzing precipitation in small area. In the system, a telecentric lens is applied with the camera to reduce the blur effect and distortion from out of focus, which is easier to measure the shape more accurately. However, the telecentric lens needs enough illuminance to capture raindrops, so a light source is also applied. Due to the limitation of camera frame rate, it is difficult to measure the raindrop shape and velocity in one measurement. That is, two different exposure time measurements, 100us and 2000us, are applied to measure shape and velocity respectively. Furthermore, the details of image processing procedures are also introduced.
Finally, three kinds of experiments were done to verify the system. Marble experiments verified the feasibility and performance of the system structure and image processing procedures. Water sprinkling experiments simulated the raining condition with more uncertainty than the marble experiment. Last, real raining condition pictures were taken and analyzed by the system. The analyzed data was compared with the data from Hsinchu weather station, Central Weather Bureau, Taiwan. An error rate of 14.42% in rain rate was obtained.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Research Motivation 3
1.3 System Structure 5
1.4 Thesis Overview 7
Chapter 2 Literature Review 8
2.1 Introduction 8
2.2 Raindrop Analysis Theories 8
2.3 Types of Disdrometers 13
2.3.1 Joss-Waldvogel disdrometer (JWD) 13
2.3.2 Acoustic disdrometer 14
2.3.3 Optical Particle Size Velocity (PARSIVEL) disdrometer 15
2.3.4 2-D Video disdrometer (2DVD) 16
2.3.5 Image-based disdrometer 17
Chapter 3 Structure of the Proposed Disdrometer and Software Algorithm 20
3.1 Introduction 20
3.2 Previous System Structure 20
3.3 Overview of the Proposed System Structure 28
3.4 The Optical Unit 30
3.5 The Image Acquisition Unit 34
3.6 The Data Processing Unit 40
3.6.1 Camera Parameter Settings 41
3.6.2 Drop Extraction and Analysis Algorithm 43
3.6.3 DSD and Rain rate Calculation 52
Chapter 4 System Verification and Raindrop Analysis 54
4.1 Introduction 54
4.2 System Setting Evaluation 54
4.2.1 Contrast Evaluation 55
4.2.2 Segmentation Evaluation 57
4.3 Marble Experiments 60
4.4 Water Sprinkling Experiments 63
4.5 Raindrop Experiments and Analysis 68
4.5.1 Raindrop Analysis 68
4.5.2 Data Comparison 76
Chapter 5 Conclusion 77
5.1 Conclusion 77
5.2 Future Work 78
Bibliography 80

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