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    Title: 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
    Authors: 張士騰
    Chang, Shih-Teng
    Contributors: 林士淵
    Lin, Shih-Yuan
    張士騰
    Chang, Shih-Teng
    Keywords: 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
    Date: 2022
    Issue Date: 2022-08-01 18:24:35 (UTC+8)
    Abstract: 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.
    Reference: 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.
    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.
    Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE conference on computer vision and pattern recognition,
    Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence(6), 679-698.
    Chang, Y.-L., Anagaw, A., Chang, L., Wang, Y. C., Hsiao, C.-Y., & Lee, W.-H. (2019). Ship detection based on YOLOv2 for SAR imagery. Remote Sensing, 11(7), 786.
    Chapelle, O., Haffner, P., & Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5), 1055-1064.
    Chen, C. W., & Zebker, H. A. (2000). Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms. JOSA A, 17(3), 401-414.
    Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., & Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155.
    Doerry, A. W. (2016). Representing SAR complex image pixels. Radar Sensor Technology XX,
    Doin, M.-P., Lasserre, C., Peltzer, G., Cavalié, O., & Doubre, C. (2009). Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models. Journal of Applied Geophysics, 69(1), 35-50.
    Duda, R. O., & Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1), 11-15.
    Fattahi, H., Simons, M., & Agram, P. (2017). InSAR time-series estimation of the ionospheric phase delay: An extension of the split range-spectrum technique. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5984-5996.
    Fernández, J., Pepe, A., Poland, M. P., & Sigmundsson, F. (2017). Volcano Geodesy: Recent developments and future challenges. Journal of Volcanology and Geothermal Research, 344, 1-12.
    Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, 49(9), 3460-3470.
    Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8-20.
    Gens, R., & VAN GENDEREN, J. L. (1996). Review Article SAR interferometry—issues, techniques, applications. International Journal of Remote Sensing, 17(10), 1803-1835.
    Ghoury, S., Sungur, C., & Durdu, A. (2019). Real-time diseases detection of grape and grape leaves using faster r-cnn and ssd mobilenet architectures. International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2019),
    Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision,
    Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition,
    Goldstein, R. M., & Werner, C. L. (1998). Radar interferogram filtering for geophysical applications. Geophysical Research Letters, 25(21), 4035-4038.
    Goutte, C., & Gaussier, E. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. European conference on information retrieval,
    Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis (Vol. 2). Springer Science & Business Media.
    Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics(6), 610-621.
    He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904-1916.
    Hosang, J., Benenson, R., Dollár, P., & Schiele, B. (2015). What makes for effective detection proposals? IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(4), 814-830.
    Hough, P. V. (1962). Method and means for recognizing complex patterns. In: Google Patents.
    Jianjun, Z., Zhiwei, L., & Jun, H. (2017). Research progress and methods of InSAR for deformation monitoring. Acta Geodaetica et Cartographica Sinica, 46(10), 1717.
    Kang, M., Leng, X., Lin, Z., & Ji, K. (2017). A modified faster R-CNN based on CFAR algorithm for SAR ship detection. 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP),
    Keen, N. (2005). Color moments. School of informatics, University of Edinburgh, 3-6.
    Keysers, D., Deselaers, T., Gollan, C., & Ney, H. (2007). Deformation models for image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1422-1435.
    Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
    LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541-551.
    Li, Z.-W., Ding, X.-L., Zheng, D.-W., & Huang, C. (2008). Least squares-based filter for remote sensingimage noise reduction. IEEE Transactions on Geoscience and Remote Sensing, 46(7), 2044-2049.
    Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition,
    Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision,
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. European conference on computer vision,
    Monti-Guarnieri, A., Parizzi, F., Pasquali, P., Prati, C., & Rocca, F. (1993). SAR interferometry experiments with ERS-1. Proceedings of IGARSS`93-IEEE International Geoscience and Remote Sensing Symposium,
    Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987.
    Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
    Pass, G., Zabih, R., & Miller, J. (1997). Comparing images using color coherence vectors. Proceedings of the fourth ACM international conference on Multimedia,
    Pathier, E., Fruneau, B., Deffontaines, B. t., Angelier, J., Chang, C.-P., Yu, S.-B., & Lee, C.-T. (2003). Coseismic displacements of the footwall of the Chelungpu fault caused by the 1999, Taiwan, Chi-Chi earthquake from InSAR and GPS data. Earth and Planetary Science Letters, 212(1-2), 73-88.
    Perissin, D., Wang, Z., & Wang, T. (2011). The SARPROZ InSAR tool for urban subsidence/manmade structure stability monitoring in China. Proceedings of the ISRSE, Sidney, Australia, 1015.
    Porzycka-Strzelczyk, S., Rotter, P., & Strzelczyk, J. (2018). Automatic detection of subsidence troughs in SAR interferograms based on circular Gabor filters. IEEE Geoscience and Remote Sensing Letters, 15(6), 873-876.
    Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition,
    Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 91-99.
    Rotter, P., & Muron, W. (2020). Automatic Detection of Subsidence Troughs in SAR Interferograms Based on Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 18(1), 82-86.
    Shrivastava, A., Gupta, A., & Girshick, R. (2016). Training region-based object detectors with online hard example mining. Proceedings of the IEEE conference on computer vision and pattern recognition,
    Tomás, R., Pagán, J. I., Navarro, J. A., Cano, M., Pastor, J. L., Riquelme, A., Cuevas-González, M., Crosetto, M., Barra, A., & Monserrat, O. (2019). Semi-automatic identification and pre-screening of geological–geotechnical deformational processes using persistent scatterer interferometry datasets. Remote Sensing, 11(14), 1675.
    Uijlings, J., Van De Sande, K., Gevers, T., & Smeulders, A. (2013). Selective search for object recognition. International Journal of Computer Vision, 104, 154-171.
    Wang, Y., Wang, C., & Zhang, H. (2018). Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images. Remote sensing letters, 9(8), 780-788.
    Wright, T., Gonzalez, P., Walters, R., Hatton, E., Spaans, K., & Hooper, A. (2016). LiCSAR: Tools for automated generation of Sentinel-1 frame interferograms. AGU Fall Meeting Abstracts,
    Xu, B., Li, Z.-w., Wang, Q.-j., Jiang, M., Zhu, J.-j., & Ding, X.-l. (2013). A refined strategy for removing composite errors of SAR interferogram. IEEE Geoscience and Remote Sensing Letters, 11(1), 143-147.
    Yan, Y., Chen, M., Shyu, M.-L., & Chen, S.-C. (2015). Deep learning for imbalanced multimedia data classification. 2015 IEEE international symposium on multimedia (ISM),
    Yu, C., Li, Z., Penna, N., & Crippa, P. (2018). Generic Atmospheric Correction Online Service for InSAR (GACOS). EGU General Assembly Conference Abstracts,
    Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes. IEEE Transactions on Geoscience and Remote Sensing, 30(5), 950-959.
    Description: 碩士
    國立政治大學
    地政學系
    109257026
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109257026
    Data Type: thesis
    DOI: 10.6814/NCCU202200756
    Appears in Collections:[Department of Land Economics] Theses

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