Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/141235
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
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。\n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。\n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。\n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.
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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
Appears in Collections:學位論文

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