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題名 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
貢獻者 邱式鴻
Chio, Shih-Hong
許家彰
Hsu, Chia-Chang
關鍵詞 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
上傳時間 2-Aug-2023 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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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
資料類型 thesis
dc.contributor.advisor 邱式鴻zh_TW
dc.contributor.advisor Chio, Shih-Hongen_US
dc.contributor.author (Authors) 許家彰zh_TW
dc.contributor.author (Authors) Hsu, Chia-Changen_US
dc.creator (作者) 許家彰zh_TW
dc.creator (作者) Hsu, Chia-Changen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 13:40:27 (UTC+8)-
dc.date.available 2-Aug-2023 13:40:27 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 13:40:27 (UTC+8)-
dc.identifier (Other Identifiers) G0110257028en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146466-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 110257028zh_TW
dc.description.abstract (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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 謝誌 I
摘要 II
Abstract IV
圖目錄 VII
表目錄 X
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 5
第三節 研究架構 6
第二章 文獻回顧 7
第一節 以物件為基礎之變遷偵測方法及原理 7
第二節 影像萃取變遷資訊之方法 16
第三節 深度學習 23
第三章 研究方法 35
第一節 研究區域 35
第二節 研究資料與處理軟體 36
第三節、研究流程 44
第四節、研究方法與理論基礎 47
第四章 實驗成果分析與討論 59
第一節 實驗資料 59
第二節 深度學習網路訓練 65
第五章 結論與建議 79
第一節 結論 79
第二節 建議 81
參考文獻 83		zh_TW
dc.format.extent 6274609 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110257028en_US
dc.subject (關鍵詞) 建物辨識zh_TW
dc.subject (關鍵詞) 建物變遷zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 數值表面模型zh_TW
dc.subject (關鍵詞) 數值高度模型zh_TW
dc.subject (關鍵詞) Building recognitionen_US
dc.subject (關鍵詞) Building change detectionen_US
dc.subject (關鍵詞) Deep learningen_US
dc.subject (關鍵詞) Digital surface modelen_US
dc.subject (關鍵詞) Digital height modelen_US
dc.title (題名) 應用深度學習於不同時期真正射影像自動偵測建物變遷zh_TW
dc.title (題名) Applying deep learning to automatically detect building changes from true orthoimages in different periodsen_US
dc.type (資料類型) thesisen_US
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