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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 韓仁毓 | zh_TW |
| dc.contributor.advisor | Jen-Yu Han | en |
| dc.contributor.author | 羅伊宸 | zh_TW |
| dc.contributor.author | Yi-Chen Lo | en |
| dc.date.accessioned | 2025-07-16T16:17:30Z | - |
| dc.date.available | 2025-07-17 | - |
| dc.date.copyright | 2025-07-16 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-08 | - |
| dc.identifier.citation | Albert, S., Aswani, S., Fisher, P. L., and Albert, J. (2009). Coral reef economic valuation and conservation in the solomon islands. Ocean Coastal Management, 52(3–4):157–165.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97796 | - |
| dc.description.abstract | 珊瑚覆蓋率對於維持海洋生物多樣性與氣候調節具有關鍵作用，石珊瑚與軟珊瑚的分布變化亦常反映海洋污染與環境惡化的狀況，因此同時監測兩者對評估珊瑚礁健康尤為重要。本研究針對傳統穿越線法(LIT)在監測範圍、主觀性及資料保存上的限制，並考量過往研究多聚焦於石珊瑚的不足，提出一套結合水下錄影、攝影測量與語意分割模型的整合性監測方法。透過高解析度正射影像建置與自動化分類流程，本研究能有效辨識石珊瑚與軟珊瑚的空間分布。實驗結果顯示， 僅需 18 分鐘錄影時間，即可重建約 80 平方公尺樣區；語意分割模型訓練中，石珊瑚與軟珊瑚的 IoU 均達 97% 以上；測試影像中石珊瑚與軟珊瑚覆蓋率誤差分別為 -1.06% 與 -0.77%，符合常見珊瑚監測計畫中 ±5% 的誤差容忍度。本研究成功建立一套低成本、高效率且具客觀性的珊瑚監測架構。 | zh_TW |
| dc.description.abstract | Coral cover plays a vital role in maintaining marine biodiversity and regulating climate. Changes in the distribution of hard and soft corals often indicate marine pollution and environmental degradation, making the simultaneous monitoring of both essential for assessing reef health. This study addresses the limitations of traditional line-intercept transects (LIT), including restricted coverage, subjectivity, and lack of data preservation, by proposing an integrated method combining underwater video, photogrammetry, and semantic segmentation. Using high-resolution orthomosaics and automated classification, our approach effectively identifies the spatial distribution of both coral types. Field experiments show that an 18-minute video can reconstruct an 80 m2 area, with the trained model achieving IoUs over 97% for both hard and soft corals. The estimated cover errors in test images were −1.06% and −0.77%, well within the ±5% tolerance commonly accepted in coral monitoring. This study establishes a low-cost, efficient, and objective coral monitoring framework. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-16T16:17:30Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-16T16:17:30Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 ............................................................................................................. i
摘要 ............................................................................................................. ii Abstract .......................................................................................................... iii 目次 ............................................................................................................. iv 圖次 .......................................................................................................... viii 表次 .......................................................................................................... ix 第一章 緒論 .............................................................................................. 1 1.1 研究背景與目的 ................................................................................ 1 1.2 論文架構 ........................................................................................... 3 第二章 文獻回顧 ..................................................................................... 4 2.1 珊瑚礁現況與生態變遷趨勢 ............................................................ 4 2.1.1 全球暖化對珊瑚礁的衝擊 ...................................................... 4 2.1.2 研究地區概況 .......................................................................... 5 2.2 珊瑚礁監測方法與技術發展 ............................................................ 6 2.2.1 人工作業調查監測方法限制 .................................................. 6 2.2.2 影像與遙感監測技術之發展與限制 ........................................ 7 2.2.3 自動化影像分類與語意分割發展 .......................................... 8 2.2.4 攝影測量與 SfM 在空間重建之應用 ...................................... 9 2.3 小結 ................................................................................................. 10 第三章 研究方法與理論 ........................................................................ 11 3.1 研究樣區與珊瑚礁環境描述 .......................................................... 12 3.2 調查區位選擇與樣區設定依據 ...................................................... 12 3.3 水下珊瑚礁調查之影像拍攝流程 .................................................. 14 3.3.1 影像擷取所用設備與參數設定 ............................................ 14 3.3.2 地面控制點影像尺度基準建立 ............................................ 15 3.3.3 三維建模之拍攝策略與規劃原則 .......................................... 15 3.3.4 潛水拍攝作業配置與流程 .................................................. 17 3.3.5 珊瑚樣區錄影資料擷取 ...................................................... 18 3.4 珊瑚影像三維建模與正射處理流程 .............................................. 18 3.4.1 SfM 初始對位與特徵點匹配 .............................................. 19 3.4.2 光束法平差與相機參數優化 .............................................. 19 3.4.3 多視角密集點雲重建 ...................................................... 20 3.4.4 三維紋理模型重建 ......................................................... 20 3.4.5 以塑膠框為基準之尺度轉換與空間校正 .............................. 21 3.4.6 珊瑚正射影像生成與輸出作業 .......................................... 22 3.5 語意分割前的珊瑚影像處理流程 ................................................ 22 3.5.1 輸入影像裁切與尺寸標準化 .............................................. 22 3.5.2 建立珊瑚標註資料 ......................................................... 23 3.6 珊瑚語意分割模型訓練與測試任務 ............................................ 24 3.6.1 DeepLabv3+ 模型結構 .................................................... 24 3.6.2 DeepLabv3+ 模型訓練參數設定 ........................................ 25 3.6.3 模型精度評估指標與選定 ................................................ 27 3.7 珊瑚影像後處理與覆蓋率分析 .................................................. 29 3.8 小結 ................................................................................................. 30 第四章 實驗結果與分析 ........................................................................ 31 4.1 珊瑚樣區水下影像調查作業 ...................................................... 31 4.2 正射影像生成與誤差評估 .......................................................... 32 4.2.1 潛水影像擷取 ....................................................................... 32 4.2.2 相機自秤定 ........................................................................... 33 4.2.3 稀疏點雲重建與視差分析 .................................................. 34 4.2.4 密集點雲與珊瑚三維紋理模型重建 ...................................... 35 4.2.5 控制尺度誤差評估 ........................................................... 36 4.2.6 珊瑚樣區正射影像生成與區塊裁切 ...................................... 37 4.2.7 裁切區塊面積估算與誤差重化 .......................................... 39 4.3 語意分割前之影像預處理工作 .................................................. 40 4.3.1 固定尺寸樣本裁切作業 .................................................. 40 4.3.2 珊瑚類別標註與資料格式轉換 .......................................... 40 4.3.3 樣本類別重分配與資料擴增處理 ...................................... 41 4.4 語意分割模型訓練與預測評估 ................................................ 41 4.4.1 訓練樣區分類表現與模型精度分析 .................................... 42 4.4.2 測試影像評估 ....................................................................... 46 4.5 本研究方法與傳統方法之效率與精度比較 .................................. 49 4.5.1 LIT 方法作業流程與限制 ................................................ 49 4.5.2 LIT 方法與本研究方法之比較 .......................................... 51 4.6 小結 ................................................................................................. 52 第五章 結論與建議 .............................................................................. 54 5.1 結論 ................................................................................................. 54 5.2 建議與未來工作 ............................................................................ 56 參考文獻 ................................................................................................. 58 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 語意分割 | zh_TW |
| dc.subject | 珊瑚覆蓋率 | zh_TW |
| dc.subject | 水下攝影測量 | zh_TW |
| dc.subject | 軟珊瑚 | zh_TW |
| dc.subject | 石珊瑚 | zh_TW |
| dc.subject | Coral cover | en |
| dc.subject | Hard corals | en |
| dc.subject | Soft corals | en |
| dc.subject | Underwater photogrammetry | en |
| dc.subject | Semantic segmentation | en |
| dc.title | 基於深度學習的珊瑚正射影像辨識 | zh_TW |
| dc.title | Deep Learning-Based Identification of Coral Orthomosaics | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 郭重言;黃春嘉 | zh_TW |
| dc.contributor.oralexamcommittee | Chung-Yen Kuo;Chun-Jia Huang | en |
| dc.subject.keyword | 石珊瑚,軟珊瑚,水下攝影測量,語意分割,珊瑚覆蓋率, | zh_TW |
| dc.subject.keyword | Hard corals,Soft corals,Underwater photogrammetry,Semantic segmentation,Coral cover, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202500937 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-08 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2030-06-25 | - |
| 顯示於系所單位： | 土木工程學系 | |
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