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題名 基於深度學習對DInSAR影像進行干涉條紋特徵檢測
Fringe Feature Detection for D-InSAR Images Based on Deep Learning
作者 張士騰
Chang, Shih-Teng
貢獻者 林士淵
Lin, Shih-Yuan
張士騰
Chang, Shih-Teng
關鍵詞 合成孔徑雷達干涉
差分合成孔徑雷達干涉
深度學習
卷積神經網路
干涉條紋偵測
InSAR
DInSAR
Deep learning
CNN
Fringe detection
日期 2022
上傳時間 1-Aug-2022 18:24:35 (UTC+8)
摘要 近年來基於合成孔徑雷達(SAR)為基礎發展的InSAR技術廣泛應用於大範圍地表觀測，從最早的單一時期差分合成孔徑雷達干涉(DInSAR)，觀測發展至多時序觀測技術(MT-InSAR)，隨著各國對於SAR衛星基礎建設的發展越趨成熟，研究人員能夠同時獲取高時間解析度以及高空間解析度的地面觀測影像，但同時大範圍且海量的資料對於常見的MT-InSAR技術來說，需具備大量的硬體儲存空間以及強大的運算能力，因此現今研究多選定以知的變形區域來縮小處理範圍，對於如何有效地的於大量資料中偵測出未知變形區域為研究課題之一。
相較於PS-InSAR、SBAS-InSAR以及DS-InSAR等多時序觀測技術，產製未經全相位恢復的DInSAR產品是相對快速的，同時包裹相位圖也能直接反映地表變形造成的相位變化，若能從大範圍包裹干涉圖中偵測地表變形產生的干涉條紋，能提供研究人員於未知變形區域的初步變形資訊及後續縮小範圍進行MT-InSAR依據。
近年來深度學習於影像分類、偵測、分割等任務中，皆展現了高於傳統機器學習的準確度，經評估後本研究嘗試採用基於Faster-RCNN變體的Libra-RCNN針對地表緩慢變形所產生的干涉條紋進行變形區域偵測以及初步變形規模分類，並建立一般性的訓練流程供後續研究參考。
研究成果顯示出基於Libra-RCNN架構訓練出的干涉條紋偵測分類模型，於實驗區域測試集其mAP指標能達到83.9% ，同時對於未標註資料集的應用測試中，展現出了模型對於未知資料集的一般性，其對於地表變形所產生的干涉條紋能達到公分級的分類成果。
Differential interferometric synthetic aperture radar (DInSAR) has been widely used in surface deformation estimation. In order to speed up the recognition of location of deformation, the wrapped phase directly reflected the phase change caused by surface deformation was used as the source in this study. Deep learning based on Libra-RCNN was applied as the tool to detect the fringes presented in the interferograms. The results show that the fringe detection and classification model trained based on the Libra-RCNN architecture can achieve 83.9% mean Average Precision(mAP) in the testing data. In addition, in the unlabeled dataset, the accuracy of centimeter level for the fringes classification generated by the surface deformation can be achieved. The testing performed in middle of Taiwan and the transferability test conducted in northern Taiwan both demonstrated the approach proposed in this study is reliable.
參考文獻 一、中文參考文獻
謝嘉聲，(2006).。以雷達干涉技術偵測地表變形研究，國立交通大學土木工程學系博士學位論文:新竹
盧玉芳，(2006).。以雷達干涉技術監測雲林地層下陷，國立交通大學土木工程學系碩士學位論文:新竹
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Anantrasirichai, N., Biggs, J., Albino, F., & Bull, D. (2019b). A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sensing of Environment, 230, 111179.
Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. (2018). Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. Journal of Geophysical Research: Solid Earth, 123(8), 6592-6606.
Anantrasirichai, N., Biggs, J., Kelevitz, K., Sadeghi, Z., Wright, T., Thompson, J., Achim, A. M., & Bull, D. (2020). Detecting Ground Deformation in the Built Environment using Sparse Satellite InSAR data with a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 2940-2950.
Askne, J. I., Dammert, P. B., Ulander, L. M., & Smith, G. (1997). C-band repeat-pass interferometric SAR observations of the forest. IEEE Transactions on Geoscience and Remote Sensing, 35(1), 25-35.
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2375-2383.
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描述 碩士
國立政治大學
地政學系
109257026
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109257026
資料類型 thesis
dc.contributor.advisor 林士淵zh_TW
dc.contributor.advisor Lin, Shih-Yuanen_US
dc.contributor.author (Authors) 張士騰zh_TW
dc.contributor.author (Authors) Chang, Shih-Tengen_US
dc.creator (作者) 張士騰zh_TW
dc.creator (作者) Chang, Shih-Tengen_US
dc.date (日期) 2022en_US
dc.date.accessioned 1-Aug-2022 18:24:35 (UTC+8)-
dc.date.available 1-Aug-2022 18:24:35 (UTC+8)-
dc.date.issued (上傳時間) 1-Aug-2022 18:24:35 (UTC+8)-
dc.identifier (Other Identifiers) G0109257026en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141235-
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) 109257026zh_TW
dc.description.abstract (摘要) 近年來基於合成孔徑雷達(SAR)為基礎發展的InSAR技術廣泛應用於大範圍地表觀測，從最早的單一時期差分合成孔徑雷達干涉(DInSAR)，觀測發展至多時序觀測技術(MT-InSAR)，隨著各國對於SAR衛星基礎建設的發展越趨成熟，研究人員能夠同時獲取高時間解析度以及高空間解析度的地面觀測影像，但同時大範圍且海量的資料對於常見的MT-InSAR技術來說，需具備大量的硬體儲存空間以及強大的運算能力，因此現今研究多選定以知的變形區域來縮小處理範圍，對於如何有效地的於大量資料中偵測出未知變形區域為研究課題之一。
相較於PS-InSAR、SBAS-InSAR以及DS-InSAR等多時序觀測技術，產製未經全相位恢復的DInSAR產品是相對快速的，同時包裹相位圖也能直接反映地表變形造成的相位變化，若能從大範圍包裹干涉圖中偵測地表變形產生的干涉條紋，能提供研究人員於未知變形區域的初步變形資訊及後續縮小範圍進行MT-InSAR依據。
近年來深度學習於影像分類、偵測、分割等任務中，皆展現了高於傳統機器學習的準確度，經評估後本研究嘗試採用基於Faster-RCNN變體的Libra-RCNN針對地表緩慢變形所產生的干涉條紋進行變形區域偵測以及初步變形規模分類，並建立一般性的訓練流程供後續研究參考。
研究成果顯示出基於Libra-RCNN架構訓練出的干涉條紋偵測分類模型，於實驗區域測試集其mAP指標能達到83.9% ，同時對於未標註資料集的應用測試中，展現出了模型對於未知資料集的一般性，其對於地表變形所產生的干涉條紋能達到公分級的分類成果。		zh_TW
dc.description.abstract (摘要) Differential interferometric synthetic aperture radar (DInSAR) has been widely used in surface deformation estimation. In order to speed up the recognition of location of deformation, the wrapped phase directly reflected the phase change caused by surface deformation was used as the source in this study. Deep learning based on Libra-RCNN was applied as the tool to detect the fringes presented in the interferograms. The results show that the fringe detection and classification model trained based on the Libra-RCNN architecture can achieve 83.9% mean Average Precision(mAP) in the testing data. In addition, in the unlabeled dataset, the accuracy of centimeter level for the fringes classification generated by the surface deformation can be achieved. The testing performed in middle of Taiwan and the transferability test conducted in northern Taiwan both demonstrated the approach proposed in this study is reliable.en_US
dc.description.tableofcontents 謝誌 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究架構 5
第二章 文獻回顧 6
第一節 InSAR技術發展現狀與理論基礎 6
一、 全球SAR雷達衛星發展現狀 6
二、 InSAR以及DInSAR 7
第二節 機器學習物件偵測算法與發展 10
一、 傳統物件偵測方法 10
二、 深度學習物件偵測方法 14
三、 物件偵測於InSAR干涉條紋發展 19
第三節 文獻回顧小結 23
第三章 研究方法 25
第一節 研究區域 25
第二節 研究資料及工具 26
一、 研究資料 26
二、 研究軟體 27
第三節 研究設計與理論基礎 29
一、 DInSAR資料集產製 30
二、 影像前處理 31
三、 訓練資料標記 32
四、 模型選擇 33
五、 精度評估 37
第四章 實驗成果與分析 38
第一節 標註資料集產製成果 39
第二節 模型訓練成果 43
一、 訓練流程簡介 43
二、 實驗成果與分析 43
第五章 成果應用與討論 48
第六章 結論與建議 51
參考文獻 53		zh_TW
dc.format.extent 24875693 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109257026en_US
dc.subject (關鍵詞) 合成孔徑雷達干涉zh_TW
dc.subject (關鍵詞) 差分合成孔徑雷達干涉zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 卷積神經網路zh_TW
dc.subject (關鍵詞) 干涉條紋偵測zh_TW
dc.subject (關鍵詞) InSARen_US
dc.subject (關鍵詞) DInSARen_US
dc.subject (關鍵詞) Deep learningen_US
dc.subject (關鍵詞) CNNen_US
dc.subject (關鍵詞) Fringe detectionen_US
dc.title (題名) 基於深度學習對DInSAR影像進行干涉條紋特徵檢測zh_TW
dc.title (題名) Fringe Feature Detection for D-InSAR Images Based on Deep Learningen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文參考文獻
謝嘉聲，(2006).。以雷達干涉技術偵測地表變形研究，國立交通大學土木工程學系博士學位論文:新竹
盧玉芳，(2006).。以雷達干涉技術監測雲林地層下陷，國立交通大學土木工程學系碩士學位論文:新竹
二、外文參考文獻
Anantrasirichai, N., Biggs, J., Albino, F., & Bull, D. (2019a). The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series. Geophysical Research Letters, 46(21), 11850-11858.
Anantrasirichai, N., Biggs, J., Albino, F., & Bull, D. (2019b). A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sensing of Environment, 230, 111179.
Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. (2018). Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. Journal of Geophysical Research: Solid Earth, 123(8), 6592-6606.
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dc.identifier.doi (DOI) 10.6814/NCCU202200756en_US