利用開源資料進行道路基礎設施製圖研究以Google影像為例

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楊杉英(Yankuba Sanyang) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

利用開源資料進行道路基礎設施製圖研究以Google影像為例
(Road Infrastructure Mapping Using Google Imagery: A Case Study of the Kanifing Municipality, The Gambia)

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摘要(中)

對於國家的社會經濟發展，具有建全與完善維護的道路網絡是重要且不容忽視的國家基礎設施。甘比亞近年正在迅速發展，開拓新的道路與改善既有的道路網絡是主要的建設項目之一。因此，本研究目的為研發有效的測繪機制，利用高解析度的影像資料建置最新的道路網絡圖資，並針對甘比亞最活耀的地區進行實例測試。
相較於傳統以像元運算為基礎的演算法，地理物件導向影像分析法(GEOBIA)能更有效的萃取出道路區域。因為其能藉由影像分割和地表分類兩個主要步驟，自高解析影像中描繪出具代表性的物件輪廓，並更易於整合至向量式的GIS平台。
影像分割是道路萃取不可或缺的處理步驟，本研究採用多尺度分割法(Multiresolution Segmentation)切割影像區塊，而其關鍵的尺度參數(scale parameter) 決定了每項切割物件異質性的最大允許量。使用傳統的試誤法雖能求解此參數，但缺乏學理支持且較主觀，故開發高自動化程度與客觀的尺度參數選定方法即為研究重點之一。因此，本研究採用影像中物件的區域變異量(local variance)自動化的選擇前述參數，此方法也進一步延伸應用在道路的幾何特性估測。道路的幾何特性評估可驗證道路尺度參數的推算是否能有效的偵測出道路輪廓。
透過影像分割後產生的物件，可藉由土地覆蓋的分類法則演算法區分其屬性類別。為了確保分類法則演算法的強健性，以分離度分析 (separability analysis)進行分類法則的篩選與物件分類後的量化評估。
結合前述的兩項主要流程，能成功且高效率地自測試案例影像中偵測出道路區域，並以多樣的量化精度評估驗證了演算法的可行性。在廣泛的評估過程中，評估了重點道路萃取的正確性與準確性。而整個評估的過程主要分為三大類: (1)寬度的正確性 (2)萃取的完整性，以及(3)整體的分類正確性。整體的道路區域萃取平均精度超過90％。

摘要(英)

The significance of an adequate and a well-maintained road network infrastructure in the socio-economic development of any country cannot be overstated. The Gambia is rapidly developing, with major emphasis placed on establishing new road networks as well as improving the quality of the existing ones. This study’s objective was to perform an efficient mapping exercise of one of the most vibrant regions of The Gambia to establish an up-to-date road network map using high-resolution images.
Geographic object-based image analysis (GEOBIA) was used to extract the roads. It was preferred to traditional pixel-based methods because of its ability to delineate readily usable objects from high-resolution imagery. Additionally, its results can be easily integrated with vector based geographic information system platforms for further analysis. It is conventionally composed of two major processes: Segmentation and Classification.
Segmentation plays an integral part in GEOBIA, and this study employed the Multiresolution Segmentation algorithm. This algorithm’s pivotal parameter, the scale parameter—which is responsible for controlling the objects’ maximum allowed heterogeneity—is conventionally selected through trial-and-error procedures that can be tedious and subjective. This study, however, adopted an automated methodology in which the selection of the mentioned parameter is based on the local variance of the image objects. This study also extended the adopted methodology to take into account the roads’ distinct geometric properties to derive the scale parameter that best segments them in the image.
The objects that were created by the segmentation algorithm were classified by developing rule sets that assigned the objects to their respective land cover classes. The separability analysis of the object classes with regard to their features was performed using a robust automatic process to ensure an efficient classification rule set development process. Combining the aforementioned two major processes ensured the realization of an efficient and objective road extraction process in the study site.
An extensive assessment process was performed to evaluate the accuracy and precision of this study’s highlighted road extraction approach. This assessment process was divided into three major categories: (1) width accuracy, (2) extraction completeness, and (3) overall classification accuracy. The highlighted road extraction approach registered an average accuracy of over 90%.

關鍵字(中)

★ 卡尼芬市
★ 岡比亞
★ 地理基於對象的圖像分析

關鍵字(英)

★ Road Infrastructure Mapping
★ Kanifing Municipality
★ The Gambia
★ Geographic Object-Based Image Analysis

論文目次

TABLE OF CONTENTS
中文摘要 i
ABSTRACT iii
ACKNOWLEDGEMENTS v
LIST OF FIGURES ix
LIST OF TABLES xii
CHAPTER 1. INTRODUCTION 1
1.1 Road Transport in The Gambia 2
1.2 Importance of Road Transport in The Gambia 3
1.3 Statement of the Problem 4
1.4 Objectives 5
1.5 Thesis Outline 6
CHAPTER 2. BACKGROUNDS 8
2.1 Road Infrastructure and Roads 8
2.2 Geographic Object-Based Image Analysis (GEOBIA) 9
2.2.1 Human Perception 10
2.2.2 The GEOBIA Paradigm 11
2.2.3 GEOBIA Development 13
2.2.4 Pixel-Based vs. GEOBIA Classification Processes 13
2.2.5 Image Objects 16
2.2.6 GEOBIA Classification 23
CHAPTER 3. RELATED WORK 27
3.1 Road Tracking Methods 27
3.2 Morphological Methods 29
3.3 Active Contour Models (Snakes) and Dynamic Programming Methods 30
3.4 Segmentation and Classification Methods 31
3.5 Summary 34
CHAPTER 4. STUDY AREA AND DATA 36
4.1 Study Area 36
4.1.1 The Gambia 37
4.2 Data 41
CHAPTER 5. METHODOLOGY 42
5.1 Segmentation 43
5.1.1 Optimal Segmentation Scale Parameter 47
5.1.2 Best Road Scale Parameter 49
5.2 Classification 49
CHAPTER 6. RESULTS AND DISCUSSION 54
6.1 Segmentation 54
6.2 Classification 56
6.3 Applying the Highlighted Extraction Method to a Large Extent of the Kanifing Municipality 59
CHAPTER 7. ACCURACY ASSESSMENT 63
7.1 Width Accuracy 63
7.2 Extraction Completeness 64
7.2.1 Total Road Pixels 64
7.2.2 Total Road Length 66
7.3 Overall Classification Accuracy 67
7.4 Effect of Spatial Resolution on the Extraction Accuracy 70
CHAPTER 8. COMPARATIVE STUDY 72
8.1 Study Area and Data 72
8.2 Executing the Comparative Analysis 73
8.3 Results and Discussions 73
8.4 Accuracy Assessment 77
CHAPTER 9. CONCLUSION 79
REFERENCES 81

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指導教授

蔡富安(Fuan Tsai)

審核日期

2014-8-8

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