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系統識別號 U0026-1111202013355100
論文名稱(中文) 應用Pleiades衛星影像萃取高雄農地範圍之不透水表面
論文名稱(英文) Extraction of Impervious Area in Agriculture Land in Kaohsiung with Pleiades imagery
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 109
學期 1
出版年 109
研究生(中文) 娜文蒂
研究生(英文) Iva Nurwauziyah
電子信箱 ivanurwauziyah@gmail.com
學號 P66067021
學位類別 碩士
語文別 英文
論文頁數 93頁
口試委員 指導教授-王驥魁
口試委員-饒見有
口試委員-Hepi Hapsari Handayani
中文關鍵字 None 
英文關鍵字 Agriculture land  Impervious area extraction  Object-based Approach  Seven high resolution Pleiades images 
學科別分類
中文摘要 None
英文摘要 The protection of agricultural land was needed to make sure the food production stability. In Taiwan, the total area of agricultural land was gradually lost due to the construction project of impervious area inside the agricultural land. In this study, an object-based approach for impervious area extraction was developed to protect the agricultural land. The image is first segmented using algorithms available in eCognition Developer software. The vegetation and water are then eliminated from the segmented regions to generate impervious area candidates. Finally, the rules integrate spectral properties and contextual relationship with its neighbor are employed to extract the impervious area. A total of seven high-resolution Pleiades images covering Kaohsiung city, Taiwan was used. The proposed method was then applied to the seven satellite images. The image-tiling procedure was used to process the seven satellite images systematically. The satellite image often contains an area covered by the cloud and leads to losing the ground target information. The seven images used were overlapped to fill in that missing object caused by the cloud. The post-processing was then employed to remove the misclassified caused by cloud. The accuracy assessment was conducted on the overlapping area of the seven satellite images. The F1-score value shows not significantly different between two overlap subsets images. The similarity test was then conducted to evaluate between two overlap objects. A high number of samples depicts the extraction result of the two overlap objects is similar.
論文目次 ABSTRACT iii
ACKNOWLEDGEMENT v
Table of Contents vi
List of Tables vii
List of Figures viii
Chapter 1 Introduction 1
Chapter 2 Study Area and Data 6
2.1 Study Area 6
2.2 Data 10
2.3 Reference Data 13
Chapter 3 Methodology 14
3.1 Object-based Approach Development 14
3.1.1 Agriculture Segment Determination 18
3.1.2 Coarse-scale Segmentation 19
3.1.3 Classification 1st Stage 25
3.1.4 Classification 2nd Stage 42
3.1.5 Fine-scale Segmentation 46
3.1.6 Classification 3rd Stage 47
3.2 Experiments 51
3.3 Cloud Removal 52
3.4 Combine Impervious Area Shapefile 54
3.5 Accuracy Assessment 55
Chapter 4 Results and Discussion 59
4.1 Evaluation of image subset 2000 x 2000 pixels 59
4.2 Similarity Test 77
Chapter 5 Conclusion 88
References 90
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