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論文名稱
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以深度學習萃取高解析度無人機正射影像之農地重劃區現況資訊
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Title
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Extracting Terrain Detail Information from High Resolution UAV Orthoimages of Farm Land Readjustment Area Using Deep Learning
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作者
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汪知馨、邱式鴻
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Author
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Chih-Hsin Wang, Shih-Hong Chio
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中文摘要
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目前多以地面測量方式執行現況測量，然該方法耗費大量人力、時間，且測量成果通常較為局部，而無人機具有低成本、快速產製高解析度正射影像的特性，故本研究採用ResU-net協助萃取高解析度無人機正射影像中農地重劃區全域的現況資訊，並分析經後處理的萃取成果應用於地籍測量相關作業之可行性。研究加入DSM探討高程對模型之助益，研究成果顯示標籤資料涵蓋高程變化處時，加入高程資訊能些微提升模型精度，宜蘭、台中測試資料F Score分別達0.73、0.86；於平面位置精度檢驗，統計得約80%資料符合相關規定，顯示應用深度學習萃取農地重劃區現況資訊有可行性。
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Abstract
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Currently, the detail data is mostly surveyed by theodolites and satellite positioning instruments; however, it is time-consuming and labor-intensive. Additionally, the surveying result is usually local data. Recently, UAVs are increasingly being used as a low-cost, efficient system which can support in acquiring high-resolution data; therefore, this study attempts to use ResU-net to assist in extracting global terrain detail information from high-resolution UAV orthoimages of farm land readjustment areas, and evaluate the feasibility of using the post-processing results in the detail survey. Except for the high-resolution orthoimages, the digital surface model (DSM) by dense matching was also used. The results showed that if the label data covered the elevation changes, adding DSM data by dense matching could promote the accuracy of detection. The F Score in Yilan testing data was 0.73; Taichung testing data was 0.86. In terms of the planar position difference, the result showed that about 80% data meet the accuracy requirement and it demonstrated the feasibility of using deep learning to assist in extracting global terrain detail information for farm land readjustment areas.
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關鍵字
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地籍測量、現況測量、深度學習、影像分割
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Keywords
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Cadastral Survey, Detail Survey, Deep Learning, Image Segmentation
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