請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71721
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 江簡富(Jean-Fu Kiang) | |
dc.contributor.author | Kai-Shiun Yang | en |
dc.contributor.author | 楊凱勛 | zh_TW |
dc.date.accessioned | 2021-06-17T06:07:40Z | - |
dc.date.available | 2021-01-08 | |
dc.date.copyright | 2019-01-08 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2019-01-02 | |
dc.identifier.citation | [1] R. Lindsay and A. Schweiger, “Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations,” Cryosphere, vol. 9, no. 1, pp. 269-283, 2015.
[2] M. C. Serreze and J. Stroeve, “Arctic sea ice trends, variability and implications for seasonal ice forecasting,” Phil. Trans. R. Soc. A, vol. 373, no. 2045, 2015. [3] J. Stroeve, M. M. Holland, W. Meier, T. Scambos and M. Serreze, “Arctic sea ice decline: Faster than forecast,” Geophys. Res. Lett., vol. 34, no. 9, 2007. [4] J. Maslanik, C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi and W. Emery, “A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss,” Geophys. Res. Lett., vol. 34, no. 24, 2007. [5] M. Arkett, D. Flett, R. De Abreu, P. Clemente-Col´on, J. Woods and B. Melchior, “Evaluating ALOS-PALSAR for ice monitoring- What can L-band do for the North American ice service?” IEEE Int. Geosci. Remote Sensing Symp., vol. 5, 2008. [6] S. E. Howell and J. Yackel, “A vessel transit assessment of sea ice variability in the western Arctic, 1969-2002: Implications for ship navigation,” Canadian J. Remote Sensing, vol. 30, no. 2, pp. 205-215, 2004. [7] W. Dierking and T. Busche, “Sea ice monitoring by L-band SAR: An assessment based on literature and comparisons of JERS-1 and ERS-1 imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 44, no. 4, pp. 957-970, 2006. [8] N. Y. Zakhvatkina, V. Y. Alexandrov, O. M. Johannessen, S. Sandven and I. Y. Frolov, “Classification of sea ice types in ENVISAT synthetic aperture radar images,” IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2587-2600, 2013. [9] “Canadian ice service digital archive-regional charts: History, accuracy, and caveats,” Rep. 00-02, Ballicater Consulting Ltd Ottawa, 2006. [10] R. G. Onstott, “Sar and scatterometer signatures of sea ice,” Microwave Remote Sensing Sea Ice, vol. 68, pp. 73-104, 1992. [11] M. Drunkwater, R. Hosseinmostafa and P. Gogineni, “C-band backscatter measurements of winter sea-ice in the Weddell Sea, Antarctica,” Int. J. Remote Sensing, vol. 16, no. 17, pp. 3365-3389, 1995. [12] T. Geldsetzer, J. B. Mead, J. J. Yackel, R. K. Scharien and S. E. Howell, “Surface-based polarimetric C-band scatterometer for field measurements of sea ice,” IEEE Trans. Geosci. Remote Sensing, vol. 45, no. 11, 2007. [13] J. A. Casey, S. E. Howell, A. Tivy and C. Haas, “Separability of sea ice types from wide swath C-and L-band synthetic aperture radar imagery acquired during the melt season,” Remote Sensing Environment, vol.174, pp. 314-328, 2016. [14] D. G. Barber, J. Yackel and J. Hanesiak, “Sea ice, RADARSAT-1 and Arctic climate processes: A review and update,” Canadian J. Remote Sensing, vol. 27, no. 1, pp. 51-61, 2001. [15] D. O. Dammann, H. Eicken, A. R. Mahoney, E. Saiet, F. J. Meyer, C. John et al., “Traversing Sea Ice-Linking Surface Roughness and Ice Trafficability Through SAR Polarimetry and Interferometry,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing, vol. 11, no. 2, pp. 416-433, 2018. [16] D. Isleifson, R. J. Galley, D. G. Barber, J. C. Landy, A. S. Komarov and L. Shafai, “A study on the C-band polarimetric scattering and physical characteristics of frost flowers on experimental sea ice,” IEEE Trans. Geosci. Remote Sensing, vol. 52, no. 3, pp. 1787-1798, 2014. [17] W. Dierking, “Mapping of different sea ice regimes using images from Sentinel-1 and ALOS synthetic aperture radar,” IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 3, pp. 1045-1058, 2010. [18] A. M. Johansson, C. Brekke, G. Spreen and J. A. King, “X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring,” Remote Sensing Environment, vol. 204, pp. 162-180, 2018. [19] S. R. Cloude and E. Pottier, “A review of target decomposition theorems in radar polarimetry,” IEEE Trans. Geosci. Remote Sensing, vol. 34, no. 2, pp. 498-518, 1996. [20] H. Liu, H. Guo and L. Zhang, “SVM-based sea ice classification using textural features and concentration from RADARSAT-2 Dual-Pol ScanSAR data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sensing, vol. 8, no. 4, pp. 1601-1613, 2015. [21] J.-S. Lee, M. R. Grunes and R. Kwok, “Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution,” Int. J. Remote Sensing, vol. 15, no.11, pp. 2299-2311, 1994. [22] J.-S. Lee, M. R. Grunes, T. L. Ainsworth, L.-J. Du, D. L. Schuler and S. R. Cloude, “Unsupervised classification using polarimetric decomposition and the complex Wishart classifier,” IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 5, pp. 2249-2258, 1999. [23] P. R. Kersten, J.-S. Lee and T. L. Ainsworth, “Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering,” IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 3, pp. 519-527, 2005. [24] J. Fan and J. Wang, “Polarimetric SAR image segmentation based on spatially constrained kernel fuzzy C-means clustering,” IEEE OCEANS, Genova, 2015. [25] A. P. Doulgeris, S. N. Anfinsen and T. Eltoft, “Automated non-Gaussian clustering of polarimetric synthetic aperture radar images,” IEEE Trans. Geosci. Remote Sensing, vol. 49, no. 10, pp. 3665-3676, 2011. [26] Z. Zhang, H.Wang, F. Xu and Y.-Q. Jin, “Complex-valued convolutional neural network and its application in polarimetric SAR image classification,” IEEE Trans. Geosci. Remote Sensing, vol. 55, no. 12, pp. 7177-7188, 2017. [27] L. Zhang, W. Ma and D. Zhang, “Stacked sparse autoencoder in PolSAR data classification using local spatial information,” IEEE Geosci. Remote Sensing Lett., vol. 13, no. 9, pp. 1359-1363, 2016. [28] S. Sandven, O. M. Johannessen, M. W. Miles, L. H. Pettersson and K. Kloster, “Barents Sea seasonal ice zone features and processes from ERS 1 synthetic aperture radar: Seasonal Ice Zone Experiment 1992,” J. Geophys. Res.: Oceans, vol. 104, no. C7, 1999. [29] V. Alexandrov, S. Sandven, K. Kloster, L. Bobylev and L. Zaitsev, “Comparison of sea ice signatures in OKEAN and RADARSAT radar images for the northeastern Barents Sea,” Canadian J. Remote Sensing, vol. 30, no. 6, pp. 882-892, 2004. [30] L.-K. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices,” IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 2, pp. 780-795, 1999. [31] S. Singha, M. Johansson, N. Hughes, S. M. Hvidegaard and H. Skourup, “Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements,” IEEE Trans. Geosci. Remote Sensing, vol. 56, no. 7, 2018. [32] S. Watts, “Radar sea clutter: Recent progress and future challenges,” IEEE Int. Conf. Radar, 2008. [33] F. Fois, P. Hoogeboom, F. Le Chevalier and A. Stoffelen, “Future ocean scatterometry: On the use of cross-polar scattering to observe very high winds,” IEEE Trans. Geosci. Remote Sensing, vol. 53, no. 9, pp. 5009-5020, 2015. [34] G. R. Valenzuela, “Theories for the interaction of electromagnetic and oceanic waves: A review,” Boundary-Layer Meteorology, vol. 13, no. 1-4, pp. 61-85, 1978. [35] F. Ulaby, R. K. Moore and A. K. Fung, Microwave Remote Sensing: Active and Passive, from Theory to Applications, vol. 3, 1986. [36] A. S. Komarov, J. C. Landy, S. A. Komarov and D. G. Barber, “Evaluating Scattering Contributions to C-Band Radar Backscatter From Snow-Covered First-Year Sea Ice at the Winter-Spring Transition Through Measurement and Modeling,” IEEE Trans. Geosci. Remote Sensing, vol. 55, no. 10, pp. 5702-5718, 2017. [37] C. E. Livingstone, K. P. Singh and A. L. Gray, “Seasonal and regional variations of active/passive microwave signatures of sea ice,” IEEE Trans. Geosci. Remote Sensing, no. 2, pp. 159-173, 1987. [38] http://iceweb1.cis.ec.gc.ca/Archive/?lang=en [39] https://earth.esa.int/web/guest/data-access [40] https://www.asf.alaska.edu [41] http://www.eorc.jaxa.jp/ALOS/en/about/palsar.htm [42] https://directory.eoportal.org/web/eoportal/satellite-missions/e/ers1 [43] P. Meadows, D. Esteban and P. Mancini, “The ERS SAR performances: An update,” Euro. Space Agency, vol. 450, pp. 79-84, 2000. [44] J.-S. Lee and E. Pottier, Polarimetric Radar Imaging: From Basics to Applications, CRC press, 2009. [45] A. Freeman and S. L. Durden, “A three-component scattering model for polarimetric SAR data,” IEEE Trans. Geosci. Remote Sensing, vol. 36, no. 3, pp. 963-973, 1998. [46] B. Minchew, C. E. Jones and B. Holt, “Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-band synthetic aperture radar,” IEEE Trans. Geosci. Remote Sensing, vol. 50, no. 10, pp. 3812-3830, 2012. [47] R. Lang, Y. Zhou, C. Utku and D. Le Vine, “Accurate measurements of the dielectric constant of seawater at L band,” Radio Science, vol. 51, no. 1, pp. 2-24, 2016. [48] I. R. Joughin, D. P. Winebrenner and D. B. Percival, “Probability density functions for multilook polarimetric signatures,” IEEE Trans. Geosci. Remote Sensing, vol. 32, no. 3, pp. 562-574, 1994. [49] S. N. Anfinsen and T. Eltoft, “Application of the matrix-variate Mellin transform to analysis of polarimetric radar images,” IEEE Trans. Geosci. Remote Sensing, vol. 49, no. 6, pp. 2281-2295, 2011. [50] J.-M. Nicolas and S. N. Anfinsen, “Introduction to second kind statistics: Application of log-moments and log-cumulants to the analysis of radar image distributions,” Trait. Signal, vol. 19, no. 3, pp. 139-167, 2002. [51] C. Tison, J.-M. Nicolas, F. Tupin and H. Maˆıtre, “A new statistical model for Markovian classification of urban areas in high-resolution SAR images,” IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 10, pp. 2046-2057, 2004. [52] S. Skrunes, C. Brekke and A. P. Doulgeris, Characterization of low-backscatter ocean features in dual-copolarization SAR using log-cumulants,” IEEE Geosci. Remote Sensing Lett., vol. 12, no. 4, pp. 836-840, 2015. [53] S. Derrode, G. Mercier, J-M. Le Caillec and R. Garello, “Estimation of sea-ice SAR clutter statistics from Pearson’s system of distributions,” IEEE Int. Geosci. Remote Sensing Symp. vol. 1, pp. 190-192, 2001. [54] Y. Hu, J. Fan and J.Wang, “Classification of PolSAR images based on adaptive nonlocal stacked sparse autoencoder,” IEEE Geoscience Remote Sensing Lett., no. 99, 2018. [55] W. Aldenhoff, C. Heuz´e and L. E. Eriksson, “Comparison of ice/water classification in Fram strait from C-and L-band SAR imagery,” Ann. Glaciology, 2018. [56] M. Dabboor, J. Yackel, M. Hossain and A. Braun, “Comparing matrix distance measures for unsupervised POLSAR data classification of sea ice based on agglomerative clustering,” Int. J. Remote Sensing, vol. 34, no. 4, pp. 1492-1505, 2013. [57] C. Liu, W. Liao, H.-C. Li, K. Fu and W. Philips, “Unsupervised classification of multilook polarimetric sar data using spatially variant Wishart mixture model with double constraints,” IEEE Trans. Geosci. Remote Sensing, 2018. [58] V. Badrinarayanan, A. Kendall and R. Cipolla, “SegNet: A deep convolutional encoderdecoder architecture for image segmentation,” IEEE Trans. Pattern Analysis Machine Intell., vol. 39, no. 12, pp. 2481-2495, 2017. [59] I. Goodfellow, Y. Bengio, A. Courville and Y. Bengio, Deep Learning, MIT Press, 2016. [60] C.-W. Hsu, C.-C. Chang, C.-J. Lin et al., “A practical guide to support vector classification,” https://www.csie.ntu.edu.tw/ cjlin/papers/guide/guide.pdf, 2003. [61] Y. Zheng, B. Jeon, D. Xu, Q. Wu and H. Zhang, “Image segmentation by generalized hierarchical fuzzy c-means algorithm,” J. Intell. Fuzzy Syst., vol. 28, no. 2, pp. 961-973, 2015. [62] D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” Annual ACM-SIAM symp. Discrete Algorithms, pp. 1027-1035, 2007. [63] Y. Zhang, M. Brady and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Trans. Med. Imag., vol. 20, no. 1, pp. 45-57, 2001. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71721 | - |
dc.description.abstract | 本論文採用監督式和非監督式的方法,處理融冰期及冬季的L波段PolSAR數據,分別對海冰進行分類。基於PolSAR共變矩陣數據中隱含的重要特徵,推論出數種類型的輸入向量,並透過模擬比較這些方法和輸入向量對海冰分類的準確率。分析不同演算法中的參數和不同輸入向量中的組成元素的效應,並以潛在的相關物理機制說明。 | zh_TW |
dc.description.abstract | Both supervised and unsupervised methods are applied to L-band PolSAR data in advanced-melt phase and winter phase, respectively, to classify sea-ice type. Several types of input vector are derived from the covariance matrix of PolSAR data,
based on the key features embedded in the data. The accuracy rate of sea-ice classification by using these methods and input vectors are compared by simulations. The effects of parameters in different algorithms and constituents in different input vectors are elaborated and related to the underlying physical mechanisms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:07:40Z (GMT). No. of bitstreams: 1 ntu-107-R05942013-1.pdf: 62586028 bytes, checksum: a674abda14b74ab05ff0649f5515025e (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 中文摘要
Abstract i Table of Contents iii List of Figures vi Acknowledgment vii 1 Introduction p.1 2 Images for Classification p.5 2.1 Radar Signature of Sea Ice p.5 2.2 Winter Phase p.7 2.3 Advanced-Melt Phase p.10 2.4 Reference Ground-Truth p.14 3 Input Vectors p.17 4 Review of Classification Methods p.21 4.1 Supervised Methods p.23 4.2 Unsupervised Methods p.25 5 Simulation Results and Discussions p.29 5.1 Results of Images in ROI 1 p.30 5.2 Results of Images in ROI 2 p.34 5.3 Results of Images in ROI 3 p.38 6 Conclusion p.46 | |
dc.language.iso | en | |
dc.title | 採用L波段PolSAR數據分類海冰之演算法及輸入向量之比較 | zh_TW |
dc.title | Comparison of Algorithms and Input Vectors for Sea-Ice Classification with L-band PolSAR Data | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 江衍偉(Yean-Woei Kiang),李翔傑(Hsiang-Chieh Lee) | |
dc.subject.keyword | 合成孔徑雷達, | zh_TW |
dc.subject.keyword | SAR, | en |
dc.relation.page | 57 | |
dc.identifier.doi | 10.6342/NTU201804412 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-01-02 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 61.12 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。