系統識別號 U0026-2608201610534400
論文名稱(中文) 應用無人飛行載具空拍照片支援全球黑面琵鷺年度普查-以形態學、物件化與局部空間統計分析處理成果為例
論文名稱(英文) Supporting the annual international black-faced spoonbill census with aerial photos acquired from an unmanned aerial vehicle: example of morphological, objective and spatial statis
校院名稱 成功大學
系所名稱(中) 地球科學系
系所名稱(英) Department of Earth Sciences
學年度 104
學期 2
出版年 105
研究生(中文) 陳易辛
研究生(英文) Yi-Hsin Chen
學號 l46034013
學位類別 碩士
語文別 中文
論文頁數 48頁
口試委員 指導教授-劉正千
中文關鍵字 黑面琵鷺  無人飛行載具  普查  任務規劃  影像處理  台江國家公園 
英文關鍵字 Black-faced spoonbill  Unmanned aerial vehicle  Census  Mission planning  Image processing 
中文摘要 全球黑面琵鷺數量大約僅存3200隻,在2003年時被國際保育聯盟(International Union for Conservation of Nature and Natural Resources)列為瀕臨絕種的保育動物。全球黑面琵鷺年度普查於1993年開始,為一項長期追蹤調查的重要工作,也是掌握越冬的黑面琵鷺之分布與數量十分有效的工具,對於野生動物管理和保護政策擬定有很大的幫助。這項工作一般是從地面上使用望遠鏡,以人工方式進行同步計數。由於黑面琵鷺並非只固定在同一個地方,所以這種計數的方式造成重複計算的可能性很高。
無人飛行載具(Unmanned Aerial Vehicle; UAV)是一個可以遠程控制,或根據飛行計劃自主飛行的載具。為了協助進行全球黑面琵鷺年度普查工作,本研究使用配備有自動導航系統之UAV,先後在2012年12月7日於台江國家公園黑面琵鷺生態保護區和2015年3月24日與2015年4月1日於頂山棲地,成功完成黑面琵鷺之高解析度照片空拍任務。並將照片做一系列的分析與處理,藉以計算黑面琵鷺之數量。
本研究根據形態學的形態交離轉換(Hit-or-Miss transform) 、物件化分析(objective analysis)和空間統計(spatial statistics analysis)三種處理方式,發展了一套半自動計數的影像處理方法,可從高解析度空拍照片上快速計算黑面琵鷺的數量。經與人工判釋的結果進行比較後發現,以形態交離轉換加上設定尺寸範圍的條件進行篩選,分類成果較佳,準確度高達97.9%。在局部空間統計的方法比較上,以使用空間統計變量Geary’s C會比Moran’s I的效果還要好。本研究成果證明應用低成本無人飛行載具拍攝高空間解析度的照片,是支援全球黑面琵鷺普查的一項有利工具。

英文摘要 The black-faced spoonbill (BFS) is a globally endangered species. The annual international BFS census (IBFSC) thus serves as an important initiative to manage and protect the population of this bird. This work attempts to investigate the feasibility of supporting the annual IBFSC with a low-cost unmanned aerial vehicle system (UAVS). The flight plan for each mission was made through the automatic mission planning system. All photographs taken in one mission were stitched to a seamless and color-balanced mosaic that can be geo-referenced and displayed on a Web-based platform, such as Google Earth. A close-up photograph of a few BFSs was acquired at a low altitude of 50 m to validate the proposed approach of image processing, including unsupervised classification, hit or miss transform, objective and spatial statistics analyses. The results show that the selection of one statistical attribute (maximum value in red channel) and two geometrical attributes (size and circularity) is appropriate for identifying BFS without ambiguity. The success of this work encourages us to conduct a large-scale deployment of UAVS in the coming IBFSC.
論文目次 摘要 I
致謝 VII
圖目錄 X
表目錄 XII
第 1 章 研究緣起與目的 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 4
第 2 章 文獻回顧 6
2.1 黑面琵鷺普查 6
2.2 遙測平台 7
2.3 影像分類與物件化辨識 8
2.3.1 光譜分類法 8
2.3.2 形態交離轉換 9
2.3.3 物件化分析 10
2.3.4 空間自相關 10
第 3 章 研究資料與方法 12
3.1 研究區域 12
3.2 研究資料 15
3.3 研究方法 21
3.3.1 影像分類 23
3.3.2 影像物件化 27
3.3.3 型態交離轉換 28
3.3.4 物件化分析 29
3.3.5 空間自相關 31
第 4 章 結果與討論 34
4.1 結果 34
4.2 討論 42
第 5 章 結論與建議 44
5.1 結論 44
5.2 建議 45
參考文獻 47
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