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研究生:詹鈞評
研究生(外文):Jyun-PingJhan
論文名稱:使用多尺度多階段物件導向影像分析技術進行崩塌地判釋
論文名稱(外文):A Multi-scale/step Object Oriented Image Analysis Scheme for Landslide Recognition
指導教授:饒見有饒見有引用關係
指導教授(外文):Jiann-Yeou Rau
學位類別:碩士
校院名稱:國立成功大學
系所名稱:測量及空間資訊學系碩博士班
學門:工程學門
學類:測量工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:77
中文關鍵詞:物件導向影像分析崩塌地偵測陰影數值地形模型
外文關鍵詞:OBIALandslide detectionShadowDEM
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莫拉克颱風於2009年8月5日至10日間為南台灣帶來大量的豪雨,並在山區造成嚴重的崩塌與土石流,如廬山溫泉區、小林村、藤枝森林遊樂區等地區。本研究即採用不同平台如衛星、空載相機、無人飛行載具(UAV),以獲取該地區之災後正射影像並結合空載光達產製之數值高程模型(DEM)與數值地表模型(DSM)進行崩塌地的影像判釋。然而傳統上進行影像判釋時,利用像元式的分類方式會造成椒鹽式雜訊使得成果並不合理,因此本研究即利用物件導向式影像分析方式(Object Oriented Analysis, OOA)提出一多尺度與多階段的崩塌地判釋方法。進行判釋時由於影像內容可能包含植被、建物、道路、雲、陰影、崩塌地等類別,因此本研究根據波譜資訊與物件分析推演出植生(NDVI/GRVI)、亮度(Brightness)、物高模型(OHM)、坡度(Slope)、密度(Density)及對比度(Contrast)等六種指標因子進行崩塌地與非崩塌地的分類。
在提出的多尺度崩塌地偵測方法中,其首先根據影像之空間解析度快速決定影像切割之初始尺度,並透過半自動的人工選取切割物件以決定各指標因子的門檻。而經切割後便利用該六種指標因子偵測崩塌地種子點作為第一階段之成果,接著利用較大的尺度進行區域成長法以合併和擴張崩塌地,經過再過濾後得到第二階段之成果。然而在區域成長法階段,因其無可避免的合併部分錯誤類別,因此便針對第二階段之成果以小尺度的再切割過濾錯誤後得到最終之精確崩塌地成果。同時由於藤枝地區受到嚴重的山丘陰影遮蔽的影響,僅使用波譜資訊無法有效偵測陰影覆蓋之崩塌地,因此另以此多尺度空間概念結合OHM指標與坡度資訊以萃取該地區之崩塌地。在成果方面發現經過三階段成果的精度評估後,本研究所提出之多尺度偵測流程可使整體精度提升至最高,同時結合陰影崩塌地之偵測方法亦可有效減少漏授比例並提升生產者精度及整體精度。
Taiwan was attacked by Typhoon Morakot during 5-10 Aug. 2009, which brings a huge amount of cumulative rainfall and caused lots of landslides in the mountainous area, such as in the Xiao Lin village, Lu San spring region, and Teng Zhi area. In this study for the purpose of performing multi-scale/step object-based landslide detection, we utilize images acquired from different platforms through Formosat-2 satellite, an airplane, and an unmanned-aerial vehicle (UAV) that equipped with DSLR digital camera as well as digital elevation model (DEM) and digital surface model (DSM) derived from airborne LiDAR system.
In traditional image analysis methods, pixel-based classification generally causes pepper and salt noise that will decrease the overall accuracy. Hence, in this study based on object-based image analysis (OBIA) we proposed a multi-scale/step landslide detection scheme. For landslide classification, considering image may contain vegetation, buildings, roads, cloud, shadow, and landslides, six feature indices are derived from spectral, topographical, and object shape information, such as vegetation index, brightness, slope, object height model (OHM), density, and contrast. In the multiresolution segmentation, image is segmented through a root-scale that is determined by the image spatial resolution and the thresholds for feature indices are determined manually by selecting some training samples. In the first, the preliminary landslide results are classified as landslide seeds and then we adopt region growing on seed objects with 1.5 times of the root-scale to expand the landslide regions. In the final, a down scale segmentation is applied on the expanded objects with a 0.75 times of the root-scale to remove some small patches errors. However, considering that landslides may be covered by shadow, we also develop a shadow landslide detection method based on the same multi-scale/step classification idea to reduce the omission errors.
After evaluation accuracy assessment through three stages of landslide detection, we conclude that the proposed scheme can optimize the results and get a maximum value of overall accuracy. Meanwhile, after shadow landslide detection the omission error is reduced. It is a major contribution of this study, particular for images acquired during the winter season or has low sun elevation angles.
摘要 I
Abstract II
致謝 III
CONTENTS IV
LIST OF TABLES VIII
LIST OF FIGURES X
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 2
1.2 Image Classification Methods 2
1.2.1 Pixel-based Image Classification 2
1.2.2 Object-based Image Analysis 3
1.3 Introduction of Landslide 4
1.3.1 Mechanism of Landslide 4
1.3.2 Landslide Types 5
1.4 Material and Study Area 8
1.5 Paper Structure 10
CHAPTER 2 IMAGE SEGMENTATION 12
2.1 Image Segmentation 12
2.2 Multiresolution Segmentation 13
2.2.1 Shape & Compactness 13
2.2.2 Scale 14
2.2.3 Layer Weighting 15
CHAPTER 3 LANDSLIDE FEATURE AND CLASSIFICATION 16
3.1 Feature indices 16
3.1.1 Vegetation Index 17
3.1.2 Brightness 17
3.1.3 Slope 18
3.1.4 Object Height Model 19
3.1.5 Density 19
3.1.6 Contrast 21
3.1.7 Summary of Class Feature 22
3.2 Classification Methods 23
CHAPTER 4 Multi-scale/step Landslide Detection 25
4.1 Initial Segmentation 26
4.2 Collection of Landslide References 27
4.3 Training Stage 27
4.4 Multi-scale/step landslide detection 28
4.4.1 Seed-point Stage 28
4.4.2 Region Growing Stage 29
4.4.3 Precise Classification Stage 30
4.5 Shadow Landslide Detection 31
4.6 Accuracy Assessment 32
CHAPTER 5 CASE STUDIES AND ANALYSES 33
5.1 Shiaolin Village 33
5.2 Lu San 35
5.3 Teng Zhi 39
5.3.1 Pre-event image with pre-event DEM 39
5.3.2 Post-event image using pre-DEM 41
5.3.3 Post-event image with post-event DEM and DSM 42
5.3.4 Post-event image with SURF matched DEM 43
5.3.5 Shadow landslide detection 45
CHAPTER 6 DISCUSSIONS 47
6.1 Landslide Detection Results of each Stage 47
6.2 Determination of Scale Parameter 48
6.3 Training Sample 50
6.4 Detected Landslide Area 50
6.5 Fuzzy Threshold Determination 51
6.6 Producer Accuracy 52
6.7 SURF matched DSM 53
6.8 Inconsistence between Ortho-image and DEM 55
6.9 OHM Index 56
6.10 Vegetation Index 56
CHAPTER 7 CONCLUSIONS 58
REFERENCES 60
APPENDIX A AUTOMATIC TIE-POINT IMAGE MATCHING 64
A.1 Review of Image Matching 64
A.1.1 Area-based image matching 64
A.1.2 Feature-based Image Matching 64
A.2 Speed-up Robust Feature (SURF) 66
A.2.1 Integral Image 66
A.2.2 Feature Detector 66
A.2.3 Feature Descriptor 67
A.2.4 Matching 68
A.3 Proposed Scheme 68
A.3.1 Matching Strategy 69
A.3.2 Robust Error Filter 70
A.3.3 Labeling 71
A.3.4 Aerial Triangulation 71
A.3.5 Back-projection Point Transfer 71
A.4 Case Study 71
A.4.1 Close-range Images 72
A.4.2 UAV Images 74
APPENDIX B CONFUSION MATRIX 75
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