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    题名: 應用深度學習於不同時期真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
    作者: 許家彰
    Hsu, Chia-Chang
    贡献者: 邱式鴻
    Chio, Shih-Hong
    許家彰
    Hsu, Chia-Chang
    关键词: 建物辨識
    建物變遷
    深度學習
    數值表面模型
    數值高度模型
    Building recognition
    Building change detection
    Deep learning
    Digital surface model
    Digital height model
    日期: 2023
    上传时间: 2023-08-02 13:40:27 (UTC+8)
    摘要: 都市建物變遷是影響都市發展的一項重要因素，都市規劃者如何有效與快速地瞭解都市環境中建物變遷便顯得格外重要。但目前大部分的建物監測作業仍依賴大量人力來進行影像辨識，不只耗時且相當費力，如果能發展一套有效的自動化影像建物變遷偵測技術，不僅節省大量時間，也能有效降低人力成本。
    本研究選擇臺北市社子島為試驗區，研究如何應用深度學習於不同時期航拍真正射影像自動偵測建物變遷。研究流程分為兩階段，第一階段使用不同時期航空彩色真正射影像，執行深度學習MS-FCN模型建物辨識研究，訓練過程中除使用真正射影像之外，亦加入數值地表模型以及數值地表模型與數值高程模型相減而得之數值高度模型，並比較三種不同型態輸入資料對於模型辨識建物精度之影響。
    第二階段以深度學習U-net模型建物變遷偵測，此階段使用前後期地形圖中建物生成之影像套疊作為訓練資料，前期地形圖中建物生成之影像與後期模型辨識之建物成果套疊做為測試資料，由於在比較兩期真正射影像間相同位置之建物變遷時，可能因兩期真正射影像使用不同成像設備而導致兩期真正射影像有些許的對位誤差，所以在模型訓練過程中，會將後期地形圖中建物生成之影像隨機移動1個pixel，藉此模擬兩期真正射影像之對位誤差，完成能抵抗對位誤差之建物變遷偵測深度學習模型。
    綜上所述，本研究將應用深度學習探討數值表面模型與數值高度模型之高程資訊於航空真正射影像自動辨識建物之助益，並利用辨識成果執行前後期建物變遷偵測，期待建立一套應用於都市建物變遷偵測之深度學習方法。在建物辨識階段，研究成果顯示相比僅利用真實正射影像，加入數值表面模型與數值高度模型之高程資訊能提升模型建物辨識能力，加入數值表面模型與數值高度模型的F1-score能達87.16%與87.65%。在建物變遷偵測階段，訓練能抵抗對位誤差之深度學習模型，其F1-score約為71.63%，根據成果顯示應用深度學習搭配高解析度真正射影像協助建物變遷偵測作業有其可行性。
    The change of urban buildings 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. Developing an effective automated image-based building change detection technology can not only save a lot of time but also significantly reduce labor costs.
    This study selects Shezi Island in Taipei City as the experimental area to investigate the application of deep learning in automatically detecting building changes in aerial true orthoimages at different time periods. The research process were divided into two stages. In the first stage, different time period aerial color orthoimages were used to conduct deep learning MS-FCN model building recognition research. In addition to using orthoimages in the training process, digital surface model (DSM) and digital height model (DHM) obtained by subtracting digital elevation model from digital surface model were also included. The three different types of input data were compared to assess their impact on the model`s accuracy in recognizing buildings.
    In the second stage, a deep learning U-net model was used for building change detection. In this stage, the images overlay generated by the buildings in the pre- and post- topographic maps were used as training data, and the images generated from the buildings in the pre-period topographic map and the results of the building recognition by the model in the post-period were overlaid as test data. Since there may be slight registration errors between the two orthoimages due to the use of different imaging devices, when comparing the building changes at the same location between the two time periods, the images generated from buildings in the post-period topographic map were randomly shifted one pixel during the model training process to simulate the registration error between the two orthoimages. This completed the building change detection deep learning model that can resist registration errors.
    In summary, this study aims to apply deep learning to describe and discuss the benefits of using elevation information from digital surface models and digital height models in the automatic recognition of buildings in aerial orthoimages. The study also aims to use the recognition results to detect building changes between different periods, with the goal of establishing a deep learning method for detecting urban building changes. The results of the building recognition stage show that adding elevation information from digital surface models and digital height models can improve the model`s building recognition ability compared to using only the aerial orthoimages. The F1-scores achieved by adding digital surface models and digital height models are 87.16% and 87.65%, respectively. In the building change detection stage, the deep learning model trained to resist registration errors achieve an F1-score of approximately 71.63%. The results demonstrate the feasibility of using deep learning in combination with high-resolution aerial orthoimages to assist in building change detection operations.
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    Zhao, Z., 2020, Multi-level Fusion Network for 3D Object Detection from Camera and LiDAR Data, Department of Electric Engineering, University of Michigan-Dearborn, Michigan.
    描述: 碩士
    國立政治大學
    地政學系
    110257028
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110257028
    数据类型: thesis
    显示于类别:[地政學系] 學位論文

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