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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150977


    Title: 應用深度學習於不同時期真實正射影像自動偵測建物變遷
    Applying Deep Learning to Automatically Detect Building Changes from True Orthoimages in Different Periods
    Authors: 邱式鴻;許家彰
    Chio, Shih-hong;Hsu, Chia-chang
    Contributors: 地政系
    Keywords: 建物辨識;建物變遷;深度學習;數值地表模型;數值高度模型
    Building recognition;Building change detection;Deep learning;Digital surface model;Digital height model
    Date: 2023-12
    Issue Date: 2024-04-29 14:18:16 (UTC+8)
    Abstract: 本研究於不同時期真實正射影像採用深度學習偵測建物變遷資訊。於第一階段以深度學習MS-FCN模型進行建物辨識，研究加入DSM與DHM探討高程對模型之助益，成果顯示相比僅使用真實正射影像，加入DSM與DHM之高程資訊能提升模型建物辨識能力，其F1-score能達87.16%與87.65%；於第二階段以深度學習U-Net模型執行建物變遷，然而在比較兩期真實正射影像間建物變遷時，可能因兩期真實正射影像有些許的對位誤差，故研究中透過將訓練資料隨機移動，訓練能抵抗對位誤差之深度學習模型，其F1-score約為71.63%，成果顯示應用深度學習搭配高解析度真實正射影像協助建物變遷偵測作業有其可行性。
    The change of urban building is an important factor influencing urban development. It is particularly important for urban planners to efficiently and quickly understand building changes in the urban environment. However, most building monitoring operations still rely heavily on manual image recognition, which is not only time-consuming but also labor-intensive. Therefore, this study uses MS-FCN and U-Net deep learning models to assist in the detection of building change information in the Shezi Island area of Taipei City from true orthoimages in different periods. In the first stage of building recognition using MS-FCN deep learning model, the study added DSM (digital surface model) and DHM (digital height model) to explore the benefits of elevation on the model. The results of the building recognition stage show that adding elevation information from DSM and DHM can improve the model's building recognition ability compared to using only the aerial true orthoimages. The F1-scores achieved by adding DSM and DHM are 87.16% and 87.65%, respectively. In the building change detection stage, the U-Net deep learning model that was trained to resist registration errors and can achieve an F1-score of 71.63%. The results demonstrate the feasibility of using deep learning in combination high-resolution aerial true orthoimages and DHM to assist in building change detection operations.
    Relation: 航測及遙測學刊, Vol.28, No.4, pp.209-226
    Data Type: article
    DOI 連結: https://doi.org/10.6574/JPRS.202312_28(4).0001
    DOI: 10.6574/JPRS.202312_28(4).0001
    Appears in Collections:[地政學系] 期刊論文

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