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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 趙鍵哲(Jen-Jer Jew) | |
dc.contributor.author | Kahn-Bao Wu | en |
dc.contributor.author | 吳康寶 | zh_TW |
dc.date.accessioned | 2021-06-08T03:31:26Z | - |
dc.date.copyright | 2019-08-16 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-12 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21337 | - |
dc.description.abstract | 近來全球氣候異常,因全球暖化導致極端氣候頻繁發生。台灣為山高、河流湍急且地質結構脆弱的地區,在極端氣候和地質脆弱兩因子的影響下,氣候變遷造成的災害已是一個不可忽視的議題。如民國98年8月的莫拉克颱風,在短時間內在南台灣降下約近一年平均降雨量,豪雨造成深層崩塌,尤其是高雄市小林村災情最為嚴重。為避免同樣的災情再次發生,判釋潛在深層崩塌特徵成為防範此類災害的首要工作,對此類災情提供事前防災工作之資訊。
政府方面,自莫拉克颱風後,委託中央地質調查所,使用航攝影像與光達之1公尺網格數值高程模型,搭配田野調查,針對台灣山區的道路和村落附近進行地質調查。以人工判釋深層崩塌發育過程中的地形特徵,如主崩崖、次崩崖、冠部裂隙、多重山脊與坡趾等,並繪製崩塌潛勢圖。該計畫成果獲得足夠的山區深層崩塌資訊和不同種類的山區圖資。 考量有效應用前述資訊與圖資,並據以開展潛在深層崩塌特徵自動化偵測研究工作,本研究使用光達之1公尺網格數值高程模型和中央地質調查所之崩塌特徵判釋成,首先以物件導向分析進行影像切割,再搭配支援向量機判釋潛在深層崩塌之特徵,並以連通分量標記法與閥值篩選分類成果,最後以剖面線分析偵測精確的崩崖位置。 本研究提出一個單以數值高程模型為輸入的自動化偵測潛在深層崩塌特徵的方法,以快速和大範圍之處理,提供含崩崖特徵的區塊目錄圖和崩崖點位圖。產出之成果可應用於崩塌邊界劃定、細部崩塌特徵偵測和崩塌目錄圖繪製。 | zh_TW |
dc.description.abstract | In Taiwan, landslides are constantly triggered by earthquakes, typhoons, and heavy rains. In August, 2009, Typhoon Morakot caused severe destruction and hundreds of people’s deaths or injuries. One of the most affected areas, Siaolin village at Kaohsiung city, was buried by deep-seated landslides in Taiwan. In order to prevent the same disaster from happening again, the detection of potential deep-seated landslide features has become the primary task of preventing such disasters, and providing information before the disaster happens.
Terrified by such an event, government started a program for manually detecting and mapping potential deep-seated landslides and features of potential deep-seated landslide near roads or villages. The light detection and ranging (LiDAR) digital elevation models (DEMs) with 1 m resolution, aerial photos and deep-seated landslide inventory maps were produced from the program. With these useful data of deep-seated landslides produced from the program, this study proposes a novel approach employing object-oriented analysis to segment LiDAR DEM and followed by labelling the segmentation, applying support vector machine to classify the scarp of landsides, using connected component labeling with threshold to refinement the classification result and marking the precise positions of the scarp features. This research proposes method with LiDAR DEM provided only is able to efficiently detect potential deep-seated landslide features with satisfactory results through an automatic work scheme. The result provides the marked regions which are contain features of potential deep-seated landslide and the position of the scarps. It can be applied to boundaries of potential deep-seated landslide demarcation and production of the landslide inventory map. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:31:26Z (GMT). No. of bitstreams: 1 ntu-108-R06521808-1.pdf: 11022661 bytes, checksum: e4e26f87e486e3c125dd56a75969ef63 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 論文架構及研究流程 2 第二章 文獻回顧 4 2.1 崩塌及深層崩塌之定義與地形特徵 4 2.2 崩塌災害評估之流程 5 2.3 崩塌影響因子 9 2.4 深層崩塌特徵之判釋 11 2.5 LiDAR DEM資料於自動化偵測深層崩塌 12 2.5.1 二維地形的描述 13 2.5.2 最大曲率法(Maximum curvature method, MCM) 14 2.5.3 等高線連結法(Contour connection method, CCM) 16 2.5.4 崩崖識別與等高線連結法(Scarp identification and contour connection method, SICCM) 20 2.6 小結 22 第三章 研究方法 24 3.1 方法流程 24 3.2 選取崩塌影響因子 28 3.2.1 目的 28 3.2.2 坡度(Slope) 28 3.2.3 曲率(Curvature) 29 3.2.4 地形粗糙度(Roughness) 30 3.2.5 地形濕度指數(Topographic wetness index, TWI) 31 3.2.6 小結 31 3.3 物件導向分析(Object-oriented analysis, OOA) 32 3.3.1 目的 32 3.3.2 物件導向分析的概念 32 3.3.3 eCognition軟體之區域成長法與區域合併法 32 3.3.4 eCognition演算法 35 3.3.5 小結 36 3.4 支援向量機(Support vector machines, SVM) 36 3.4.1 目的 36 3.4.2 支援向量機的概念 37 3.4.3 支援向量機的基礎理論 37 3.4.4 超平面(Hyperplane) 38 3.4.5 支援向量(Support vector) 41 3.4.6 核函數(Kernel function) 41 3.5 連通分量標記法 42 3.5.1 目的 42 3.5.2 區塊還原至影像 43 3.5.3 連通之定義 44 3.5.4 連通分量標記法之範例說明 45 3.5.5 閥值設定與篩選 47 3.6 剖面線分析 48 3.6.1 目的 48 3.6.2 崩崖點位之定義 48 3.6.3 剖面線分析之概念 48 3.6.4 剖面線分析改良 51 第四章 實驗成果與分析 52 4.1 實驗設備和平台 52 4.2 實驗區介紹 52 4.3 資料處理流程 54 4.4 參數設定 61 4.4.1 物件導向分析之參數設定 61 4.4.2 支援向量機之參數設定 62 4.4.3 連通分量標記法之參數設定 63 4.4.4 剖面線分析參數設定 65 4.5 精度評估指標 65 4.5.1 支援向量機的成果評估 65 4.5.2 連通分量標記法的成果評估 66 4.5.3 剖面線分析的成果評估 66 4.6 實驗成果 66 4.6.1 物件導向分析之成果 66 4.6.2 支援向量機之分類成果 67 4.6.3 連通分量標記法篩選之成果 68 4.6.4 剖面線分析之成果精度 69 4.6.5 剖面線分析改良 69 4.6.6 研究方法耗時估計 70 4.7 成果分析 70 4.7.1 崩塌影響因子分析 70 4.7.2 支援向量機成果分析 72 4.7.3 連通分量標記法成果分析 73 4.7.4 剖面線分析成果說明 74 4.7.5 多種改良剖面線分析方法比較 76 4.7.6 耗時成果評估 76 4.8 與SICCM成果比較 76 4.9 小結 77 第五章 結論與建議 78 5.1 結論 78 5.2 未來研究之建議 79 參考文獻 80 | |
dc.language.iso | zh-TW | |
dc.title | 利用LiDAR DEM以物件導向分析搭配支援向量機進行潛在深層崩塌特徵自動化偵測 | zh_TW |
dc.title | Automatic Detection of Potential Deep-Seated Landslide Features from LiDAR DEM Using Object-Oriented Analysis with Support Vector Machine | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡展榮(Jaan-Rong Tsay),邱式鴻(Shih-Hong Chio),莊子毅(Tzu-Yi Chuang) | |
dc.subject.keyword | 深層崩塌,數值高程模型,光達,物件導向分析,支援向量機, | zh_TW |
dc.subject.keyword | deep-seated landslide,DEM,LiDAR,object-oriented analysis,support vector machine, | en |
dc.relation.page | 85 | |
dc.identifier.doi | 10.6342/NTU201903178 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2019-08-13 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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